DCS health management platform based on agile development and general-branch architecture

Abstract

The invention belongs to the technical field of nuclear power DCS health management, and aims to provide a DCS health management platform for nuclear power plant DCS operation and maintenance business application. The invention discloses a DCS (Distributed Control System) health management platform based on agile development and a general-branch architecture, which comprises a display layer, a business application layer, a model and service layer, an operation and maintenance deployment layer and an infrastructure layer, and is characterized in that intelligent operation and maintenance management, software and hardware state remote monitoring and service life prediction of a nuclear power plant DCS are realized through the business application layer; and a dynamic logic deduction and fault tracing mechanism is constructed, full-process digital operation management and control, overall planning of a maintenance plan, authority auditing and intelligent guiding are carried out, and a full-life-cycle data system is established. According to the invention, DCS operation and maintenance service application is provided for the nuclear power plant, the reliability management of the DCS of the nuclear power plant and the risk analysis level of an instrument control system can be improved, and the probability of unexpected unit operation events caused by the DCS is reduced.

Description

Technical Field

[0001] This application belongs to the field of nuclear power DCS health management technology, and in particular relates to a DCS health management platform based on agile development and a distributed architecture. Background Technology

[0002] With the development of digital technology, all newly built power plants and most operating nuclear power plants now use DCS systems, and some older power plants that used analog instruments are also gradually undergoing digital control upgrades. As the nervous system and brain of a nuclear power plant, the DCS system has a global and systemic impact on the unit. If any link in the DCS operation deviates, it may cause the degradation of related equipment in the nuclear power plant, and in severe cases, it may even lead to reactor shutdown, turbine shutdown, and reduced unit power, posing a great challenge to the safe, reliable, and economical operation of the nuclear power plant. Therefore, conducting research on DCS health management technology and gradually improving the reliability and intelligent operation and maintenance level of the DCS system plays a crucial role in the safe and stable operation of the unit. Summary of the Invention

[0003] The main purpose of this application is to provide a DCS health management platform based on agile development and a distributed architecture. It is a DCS health management platform implemented based on containerization technology and microservice architecture, providing DCS operation and maintenance business applications for nuclear power plants.

[0004] Another objective of this application is to provide a DCS health management platform based on agile development and a distributed architecture. This DCS health management platform is implemented based on containerization technology and microservice architecture, integrating technologies such as virtual simulation, 3D simulation, process simulation, and data communication. It realizes fault inference, event tracing, instrumentation and control equipment classification, dynamic logic diagrams, AI intelligent maintenance, 3D model of control cabinet, configuration change verification, fault risk analysis, DCS layer 2 lightweighting, DCS network simulation model, and process and instrumentation and control models.

[0005] Another objective of this application is to provide a DCS health management platform based on agile development and a distributed architecture, a DCS health management data warehouse implemented based on containerization technology and microservice architecture, to build a unified DCS health management data standard system, to provide a wealth of DCS data management tools, and to realize the governance and integration of multi-source heterogeneous data.

[0006] Another objective of this application is to provide a DCS health management platform based on agile development and a distributed architecture, and a DCS health management cockpit implemented based on containerization technology and microservice architecture. It uses visualization methods, various graphics and charts, to provide a vivid and model-based description, reflecting the characteristics and trends of indicators. It uses scientific and novel indicator icons, and selects appropriate color schemes, fonts, components and special effects (dynamic effects) to provide a human-computer interaction interface that conforms to business processes and work habits for different user roles.

[0007] Another objective of this application is to provide a DCS health management platform based on agile development and a distributed architecture, a unified authentication user center system for DCS health management implemented based on containerization technology and microservice architecture, and a shared DCS health management platform built using cloud-native, SaaS multi-tenant isolation and other technologies, enabling group DCS management for various power plants.

[0008] To achieve the above objectives, this application provides the following technical solution: A DCS health management platform based on agile development and a distributed architecture includes: The display layer is used to monitor the operation and maintenance status of the DCS, and to provide early warnings and analysis of abnormal key indicators. The business application layer is used for intelligent operation and maintenance management of nuclear power plant DCS systems, remote monitoring and life prediction of software and hardware status, construction of dynamic logic deduction and fault tracing mechanism, integration of AR technology to realize full-process digital operation control, overall maintenance plan, access audit and intelligent guidance, and establishment of a full life-cycle data system. The model and service layer is used to sort and analyze a large amount of DCS system-related operation and maintenance data in nuclear power plants, and to build DCS parallel systems, large DCS models, and DCS health assessment models. This provides support for DCS simulation operation and risk analysis, DCS operation and maintenance management, and DCS health assessment. This data contains rich knowledge and information, which is of great value for optimizing operation and maintenance strategies, improving office efficiency, and assisting decision-making. Based on data warehouse and large language model technology, this platform builds an intelligent operation and maintenance system that can support knowledge collection, analysis and management, push and application, and serve the actual work of nuclear power plants. The operations and maintenance (O&M) deployment layer is used to implement an integrated development and operations platform throughout the entire lifecycle of the nuclear power DCS health management platform. This platform encompasses requirements management, test management, code management, continuous integration, continuous deployment, and system operations and maintenance. The O&M deployment layer provides an automated code deployment process, which improves software development and deployment efficiency. At the same time, it checks the outputs with third-party platforms in accordance with unified specifications to ensure the reliability and stability of the system. The infrastructure layer provides hardware support, operating system, network security, and resource management for the platform's development. The infrastructure layer includes cluster deployment, elastic computing services, distributed collaborative services, distributed file systems, security management, open storage services, remote procedure calls, task scheduling services, resource management, network resources, operating system, and cluster monitoring.

[0009] As an implementable approach, the business application layer includes: The equipment health monitoring module is used to remotely monitor the hardware, software and network status of the DCS system in real time, store and manage instrumentation and process alarm data, perform qualitative and quantitative health analysis on the overall system and its parts, and predict the short and long lifespan of the components. The simulation operation and risk analysis module is used to parse the DCS configuration logic to generate a visual dynamic logic diagram, simulate equipment failure to deduce the impact path of events, verify the risk consequences of change plans, evaluate the criticality level of instrumentation and control equipment through fault injection, and trace the initial signal source and root cause of transient events. The maintenance management module is used to coordinate the entire equipment maintenance process, and integrates functions such as maintenance plan push and display, work order overdue warning and data statistical analysis. The DCS operation and maintenance large model module is used for the digital management and control of the entire DCS operation and maintenance process in nuclear power plants, enabling operation compliance verification and image traceability. The equipment basic information management module is used to build a data management system for the entire life cycle of nuclear power DCS system equipment and integrate multi-source data resources. The configuration management module is used to control the changes and management of DCS configuration items in nuclear power plants.

[0010] As one feasible approach, the equipment health monitoring module includes: The DCS status monitoring unit is used to remotely monitor and troubleshoot DCS system hardware, software and network through data acquisition, storage and visualization. The alarm management unit is used to classify, store, and query instrumentation alarm information and process alarm information from the DCS system. The DCS health assessment unit is used to combine feature data and health assessment algorithms to perform qualitative and quantitative health status assessment and prediction of the DCS system. The card life management unit is used to generate short-term and long-term life prediction results based on card feature data and life prediction algorithms to support operation and maintenance decisions.

[0011] As one feasible approach, the DCS condition monitoring unit includes: The communication interface subunit is used to collect monitoring data from the DCS system. The data processing subunit is used to process the collected data and store it in the DCS health management data warehouse; The visualization display sub-unit is used to retrieve real-time status data from the database and present it visually.

[0012] As one feasible approach, an alarm management unit includes: The instrument control alarm subunit is used to store and query historical alarm information of the DCS system; The process alarm subunit is used to store and query historical process alarm information related to DCS health management operations.

[0013] As an implementable approach, the DCS health assessment unit includes: The qualitative evaluation subunit is used for qualitative health evaluation based on an expert rule base. The quantitative evaluation subunit is used for quantitative health evaluation based on numerical analysis models. The predictive analysis subunit is used to predict the development trend of the system's health status by combining time series data.

[0014] As an implementable approach, the card lifecycle management unit includes: The short-term lifetime prediction subunit is used for degradation analysis based on real-time operational data. Long-term life prediction sub-unit, used for life assessment based on stress-intensity model; The operation and maintenance decision support subunit is used to generate maintenance recommendations based on lifetime prediction results.

[0015] As an feasible approach, the simulation operation and risk analysis module includes: The signal flow diagram unit is used to parse the DCS configuration logic and access the unit operation data to generate a visual logic diagram to display the current logic status. The fault simulation unit is used to simulate DCS faults through instrumentation and control system models and process simulation models, and to deduce the event process and impact. The change verification unit is used to implement change plans in the DCS parallel system and verify the operational status and risk consequences after the change. The instrumentation and control equipment classification tool unit is used to determine the criticality level of equipment based on equipment fault injection and response monitoring, combined with preset classification rules. The event tracing unit is used to locate the source of the initial signal of a transient event by tracing the initial signal, and to analyze the cause of the event by using a fault tracing model. The dynamic logic diagram unit is used to parse the DCS configuration file to generate a graphical logic topology and integrate dynamic operation data with PI system calculation data.

[0016] As one feasible approach, the signal flow graph unit includes: DCS configuration logic parsing subunit is used to extract logic operation rules; The real-time data access subunit connects to the unit's operating database to obtain input data; The visualization engine subunit is used to render the results of logical operations into dynamic logic diagrams, supporting logic state monitoring, signal tracing, and anomaly location.

[0017] As an feasible approach, the fault simulation unit includes: The fault scenario configuration subunit is used to set the DCS fault type and parameters; Multi-model coupling subunit is used to synchronously drive the dynamic interaction between the instrumentation and control system model and the process simulation model; The impact assessment subunit is used to generate reports on fault propagation paths and consequences, providing quantitative basis for maintenance strategies.

[0018] As an implementable approach, the change verification unit includes: The change plan import sub-unit is used to receive DCS transformation plans and configuration files; Virtual implementation subunits are used to deploy change logic in a DCS parallel system; The risk simulation subunit is used to predict operational risks after changes based on historical data and simulation models, and output verification results and optimization suggestions.

[0019] As an implementable approach, the event sourcing unit includes: The initial signal tracing subunit is used to locate the initial signal source of a transient event through time series analysis and signal correlation matching; The fault tracing model subunit is used to reverse-engineer the equipment fault chain based on knowledge graphs and reasoning algorithms, and output diagnostic conclusions and root causes of the fault.

[0020] As an implementable approach, dynamic logic diagram units include: The configuration file parsing subunit is used to extract signal points and topological relationships and generate a logic diagram framework. The data encapsulation subunit is used to dynamically bind the operating data of the DCS parallel system and the PI system data to the logic diagram; The visual interaction sub-unit is used to support logical layer-level navigation, real-time data refresh, and historical data playback.

[0021] As an feasible approach, the maintenance management module includes: The maintenance planning management unit is used for full-process control of DCS operation and maintenance activities in nuclear power plants, enabling dynamic information display of maintenance activities, early warning of overdue work orders, and multi-dimensional statistical analysis of maintenance data. The tool and equipment management unit is used to build a master database of tools and equipment materials, realize the full life cycle management of tools and equipment in terms of warehousing registration, usage tracking, and maintenance records, and configure an overdue return early warning mechanism; The maintenance document management unit is used for standardized archiving management of maintenance documents. It integrates a document classification index engine, version control module and automatic review reminder system, supports online approval process management, and has a periodic review triggering mechanism based on document type and validity verification function. The maintenance time statistics unit is used to collect time data, generate statistical reports, provide multi-dimensional charts and graphs, and establish a data interface with the performance appraisal system. The authorization management unit is used to build the RBAC permission management system, realize hierarchical permission configuration, technical authorization identification and matching, and authorization certificate expiration warning, and support fine-grained permission allocation at the organizational level; The intelligent maintenance unit is used to convert maintenance procedures into structured electronic work orders, enabling automatic guidance of maintenance steps, real-time verification of operational compliance, and process image traceability.

[0022] As an implementable approach, the DCS operations and maintenance large model module includes: The knowledge base management unit is used to build and maintain a dedicated knowledge repository for the nuclear power DCS system, and to classify and manage historical data and documents of the nuclear power plant through a structured storage engine. The intelligent dialogue unit is used to provide natural language interaction services, supporting users to achieve intelligent retrieval and question-and-answer functions through semantic understanding and intent recognition technologies; The Office Assistant unit is used to assist in the operation and maintenance office scenarios of nuclear power DCS, automatically generating technical documents and reports that conform to industry standards, and performing semantic interpretation and key information extraction of unstructured documents; The data service unit is used to provide intelligent services for the operation data of nuclear power DCS systems, automatically generate interactive visualization charts, and realize data insights and anomaly pattern recognition. The Operation and Maintenance Assistant unit is used to realize the intelligent operation and maintenance of the entire nuclear power DCS system, forming a closed-loop knowledge update mechanism for fault mode analysis, handling process recording and preventive measures summary.

[0023] As an feasible approach, the equipment basic information management module includes: The equipment management unit is used to build a database of the entire life cycle of nuclear power DCS system equipment. It uses a data standardization engine to uniformly model the basic information of equipment at levels 0, 1, and 2, and establishes a visual mapping model of equipment hierarchical topology. The spare parts management unit is used to realize the holographic file management of spare parts in the nuclear power DCS system, perform structured storage, support dynamic maintenance of the spare parts tree classification architecture, and provide spare parts map matching and cross-field fuzzy search functions based on image recognition. The preventive maintenance management unit is used to synchronize the preventive maintenance outline in real time, establish a pre-maintenance cycle optimization model based on the characteristics of DCS equipment, and output a pre-maintenance project execution effectiveness evaluation report and maintenance strategy adjustment suggestions. The periodic test management unit is used to establish a periodic test intelligent monitoring platform, realize the automatic synchronization of test outlines, and build a test data deviation early warning model to identify abnormal fluctuation trends. The equipment basic information management module is used to integrate the nuclear power DCS system operation and maintenance basic data resource pool, and provide equipment failure mode statistical analysis, spare parts demand prediction model and maintenance outline optimization scheme.

[0024] As an implementable approach, a preventative maintenance management unit includes: The outline management subunit is used for real-time synchronization of preventive maintenance outlines, dynamically generating statistical charts of outline item compliance and heat maps of version iteration differences; The project management sub-unit is used to output a pre-maintenance project execution effectiveness assessment report and maintenance strategy optimization suggestions through multi-factor correlation analysis of equipment health and maintenance records.

[0025] As an implementable approach, the configuration management module includes: The equipment file management unit is used to manage manufacturer files, system diagrams, operation and maintenance files, configuration files, equipment classification results, etc. for instrumentation and control equipment in nuclear power plants. It provides functions such as equipment file upload, version control, structured storage, and fast retrieval for instrumentation and control system operation and maintenance, ensuring the traceability and consistency of files. The setting management unit is used for unified information management and maintenance of the parameter setting values ​​of the instrumentation and control system; The temporary mandatory management unit is used to manage the power plant's temporary mandatory documents and processes, monitor the signal status of DCS mandatory points, and assist the power plant in completing temporary mandatory work. The change management unit is used to manage power plant change documents. In conjunction with the DCS parallel system, it links with the change verification module in the simulation operation and risk analysis system to assist in the power plant change verification work and realize the calculation of probability risk indicators for changes.

[0026] As an implementable approach, the model and service layer includes: The DCS parallel system is used to build a complete mapping environment for the reference unit's DCS hardware and software systems and equipment, realize the fault simulation function of various DCS equipment, and provide a verification environment and training platform for operation and maintenance activities such as DCS change verification, fault risk analysis, fault tracing and simulation, maintenance plan formulation and personnel skills training. The DCS large model module is used to improve the coverage, accuracy and interpretability of DCS domain knowledge and reduce dependence on labeled data; The algorithm service module provides health assessment, card life prediction, and large model algorithms to support the platform's intelligent analysis and decision-making functions. The DCS health management data warehouse is used to integrate multi-source heterogeneous data, provide collection, storage and processing services, support the data needs of various application modules, and provide a unified data support platform for various application modules of DCS health management.

[0027] As an feasible approach, the operations and maintenance deployment layer includes: The logging subsystem is used to uniformly manage all operational logs of the DCS health management platform and collect logs from various subsystem microservices. The monitoring subsystem is used to monitor and provide early warning of the overall operating indicators of the DCS health management platform, and to view the memory, network throughput, disk and CPU of running servers, middleware and business services in real time.

[0028] Compared with existing technologies, the DCS health management platform based on agile development and a distributed architecture provided in this application has the following advantages: This application combines digitalization, virtual simulation, artificial intelligence, cloud platform, big data algorithms, and other technologies with a DCS prototype to realize a DCS health management platform with technical modules such as instrumentation and control equipment classification, DCS reliability management, DCS parallel system, DCS status monitoring, DCS health assessment, DCS life management, fault simulation, event tracing, DCS maintenance management, and DCS data warehouse. This forms a general DCS health management technology solution, improves the reliability management of nuclear power plant DCS and the risk analysis level of instrumentation and control systems, and reduces the probability of unexpected unit operation events caused by DCS.

[0029] The DCS health management platform enhances the power plant's intelligent management, decision-making, and monitoring capabilities, significantly reducing the workload of maintenance personnel. With the widespread adoption of this technology, it will help achieve efficient instrumentation and control system maintenance.

[0030] The DCS health management platform integrates large-scale model technology, uses the DCS data warehouse as its data foundation, and achieves intelligent interactive question and answer based on the knowledge base. During use, it generates a large amount of training data related to the nuclear industry, providing data support for the training of large-scale models and terminal intelligent brains in the nuclear industry.

[0031] For nuclear power plant instrumentation and control maintenance personnel, this application provides a series of application and management services, including DCS status monitoring, DCS health assessment, DCS card life prediction, signal flow diagrams, DCS equipment data association networks, and instrumentation and control experience feedback.

[0032] This application relies on domestic and international equipment reliability management technical standards to form a systematic solution for DCS health management, including unified standards, processes, methods, tools, and platforms.

[0033] This application utilizes SaaS multi-tenant isolation technology to develop a unified database platform and establish unified analysis and modeling standards, laying the foundation for subsequent cluster DCS management, thereby reducing nuclear power plant production and management costs, optimizing resource allocation, and improving the safety and economy of the power plant. Attached Figure Description

[0034] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the technical description will be briefly introduced below.

[0035] Figure 1 This is a schematic diagram of the technical architecture of the DCS health management platform provided in this application; Figure 2 This is a schematic diagram of the DCS health management platform system architecture provided in this application; Figure 3 This is a schematic diagram of the data processing flow of the DCS health management platform data warehouse provided in this application; Figure 4 This is a schematic diagram of the DCS remote status monitoring data transmission scheme provided in this application; Figure 5 This is a schematic diagram illustrating the functional design of the DCS health assessment algorithm provided in this application. Figure 6 This application provides a schematic diagram of the DCS health management data warehouse system architecture. Figure 7 This is a schematic diagram of the DCS health management platform configuration change verification process provided in this application. Figure 8 This application provides a schematic diagram of the DCS fault simulation function flowchart. Figure 9 A schematic diagram illustrating the design of the DCS event tracing function provided in this application; Figure 10 A schematic diagram of the process design for the instrumentation and control equipment classification tool provided in this application; Figure 11 This is a schematic diagram of the functional design of the large DCS operation and maintenance model provided in this application; Figure 12This is a schematic diagram of the DCS health management multi-power plant cluster management architecture provided in this application. Detailed Implementation

[0036] The following detailed description provides further details on specific implementation methods.

[0037] like Figure 1 As shown, the DCS health management platform constructed in this application adopts the B / S model and is designed based on cloud technology, containerization, virtualization and microservices. It is designed in a layered and modular way and integrates the reliability standard specification system and security protection system into the entire architecture design. It provides nuclear power plants with business functions such as equipment health monitoring, simulation operation and risk analysis, maintenance management, DCS operation and maintenance large model, equipment basic information, and configuration management.

[0038] like Figures 1 to 12 As shown in the embodiment of this application, a DCS health management platform based on agile development and a distributed architecture is provided. The platform architecture is divided into five layers from top to bottom: display layer, business application layer, model and service layer, operation and maintenance deployment layer, and infrastructure layer.

[0039] The display layer, implemented using web front-end technology, uses various charts to intuitively monitor the operation and maintenance of the DCS and to provide early warnings and analysis of abnormal key indicators, forming a data-driven and efficient operation and maintenance monitoring and management model.

[0040] The business application layer is used for intelligent operation and maintenance management of nuclear power plant DCS systems, remote monitoring of hardware and software status and life prediction, construction of dynamic logic deduction and fault tracing mechanism, integration of AR technology to realize full-process digital operation control, overall coordination of maintenance plan, access audit and intelligent guidance, and establishment of a full life cycle data system.

[0041] The model and service layer, based on large-scale model technology, utilizes a vast amount of DCS system-related operation and maintenance data from nuclear power plants. This data contains rich knowledge and information, which is of significant value for optimizing operation and maintenance strategies, improving office efficiency, and supporting decision-making. Therefore, this platform, based on data warehouse and large-scale language model technology, constructs an intelligent operation and maintenance system that truly serves the actual work of nuclear power plants, supporting everything from knowledge acquisition to analysis and management, and finally push and application.

[0042] At the operations and maintenance deployment layer, an integrated development and operations platform is adopted throughout the entire lifecycle to achieve integrated management of the nuclear power DCS health management platform, including requirements management, test management, code management, continuous integration, continuous deployment, and system operations and maintenance.

[0043] The infrastructure layer, based on DHP private cloud technology, provides hardware support, operating system, network security, and resource management for the development of the DCS health management platform. The infrastructure layer mainly includes cluster deployment, elastic computing services, distributed collaboration services, distributed file systems, security management, open storage services, remote procedure calls, task scheduling services, resource management, network resources, operating system, and cluster monitoring.

[0044] The display layer is implemented using web technologies, commonly including JavaScript, HTML / CSS, Element UI, Node.js, Vue, WebSockets, AntvG6, and Vue functional and business components. Based on the needs and usage scenarios of DCS operation and maintenance in nuclear power plants, the display layer aims to realize the human-machine interaction and comprehensive display functions of the DCS health management platform's cockpit. It provides human-machine interfaces that conform to the business processes and work habits of different user roles; simultaneously, it uses charts, pie charts, and other methods to intuitively display the DCS operation and maintenance status and provide early warnings and analysis of abnormal key indicators, forming a data-driven, efficient operation and maintenance monitoring and management model, and improving the capabilities of refined operation and maintenance, intelligent decision-making, and precise service.

[0045] This embodiment designs the business application layer as including an equipment health monitoring module, a simulation operation and risk analysis module, a maintenance management module, a DCS operation and maintenance large-scale model module, an equipment basic information management module, and a configuration management module. The business application layer provides a series of application and management services to nuclear power plant instrumentation and control operation and maintenance personnel. Specifically, the business application layer includes: The equipment health monitoring module is used to remotely monitor the hardware, software and network status of the DCS system in real time, store and manage instrumentation and process alarm data, and perform qualitative and quantitative health analysis on the overall system and local parts based on feature data and health evaluation algorithms, predict the long and short life of the card, and ultimately provide decision support for DCS operation and maintenance. The simulation operation and risk analysis module is used to parse the DCS configuration logic to generate a visual dynamic logic diagram, simulate equipment failure to deduce the impact path of events, verify the risk consequences of change plans, evaluate the criticality level of instrumentation and control equipment through fault injection, trace the initial signal source and root cause of transient events, and integrate real-time unit data and PI system calculation results to provide decision support for risk warning and operation optimization. The maintenance management module is used to coordinate the entire equipment maintenance process, integrating functions such as maintenance plan push and display, work order overdue warning and data statistical analysis. It realizes full life cycle management of tools and equipment and overdue borrowing reminders, supports electronic archiving, classification review and periodic verification of maintenance documents, provides manual time record statistics and visual chart analysis, combines hierarchical permission configuration, technical authorization identification and overdue warning control, and realizes wearable operation guidance, electronic procedure error correction and intelligent monitoring of DCS cabinet maintenance process based on AR technology, comprehensively improving maintenance efficiency and standardization. The DCS operation and maintenance big model module is used for the digital management and control of the entire DCS operation and maintenance process in nuclear power plants. It integrates dynamic monitoring of maintenance plans, full life cycle management of tools and equipment, standardized document archiving and online approval, work hour statistics and performance docking, RBAC permission level and safety audit, and relies on AR guidance and wearable device interaction to realize operation compliance verification and image traceability, thereby improving operation and maintenance efficiency and standardization. The equipment basic information management module is used to build a data management system for the entire life cycle of nuclear power DCS system equipment, integrating multi-source data resources for equipment management, spare parts management, preventive maintenance management, and periodic test management; The configuration management module is used to control the changes and management of DCS configuration items in nuclear power plants (such as DCS equipment files, parameter settings, temporary / permanent change procedures, and temporary mandatory procedures) to ensure the integrity, consistency, and traceability of data, files, knowledge, and processes generated or required during operation and maintenance.

[0046] In one embodiment, the device health monitoring module includes: The DCS status monitoring unit is used to remotely monitor and troubleshoot DCS system hardware, software and network through data acquisition, storage and visualization. The alarm management unit is used to classify, store, and query instrumentation alarm information and process alarm information from the DCS system. The DCS health assessment unit is used to combine feature data and health assessment algorithms to perform qualitative and quantitative health status assessment and prediction of the DCS system. The card life management unit is used to generate short-term and long-term life prediction results based on card feature data and life prediction algorithms to support operation and maintenance decisions.

[0047] The DCS status monitoring unit enables remote status monitoring of the DCS system's hardware, software, and network, and provides support for handling DCS faults. Monitoring data is collected via a communication interface, processed, and stored in the DCS health management data warehouse. The DCS status monitoring unit retrieves real-time status data from the database and uses data visualization technology to achieve DCS status monitoring.

[0048] Alarm management is divided into instrumentation alarms and process alarms. Instrumentation alarms store historical alarm information (fault information) from the DCS system and provide convenient querying. Process alarms store and query historical alarm information (fault information) related to DCS health management business.

[0049] DCS health assessment is based on various characteristic data of the DCS system and combined with the current mainstream health assessment algorithms to evaluate and predict the overall and local health status of the DCS system. It includes qualitative and quantitative assessments, and the assessment results are used to assist DCS operation and maintenance work.

[0050] Card life management is based on the structural composition and various characteristic data of the cards, combined with the currently common life prediction algorithms in the industry, to predict the life of DCS system cards, including short-term life prediction and long-term life prediction. The evaluation results are used to assist DCS operation and maintenance.

[0051] In one embodiment, the DCS status monitoring unit includes: The communication interface subunit is used to collect monitoring data from the DCS system. The data processing subunit is used to process the collected data and store it in the DCS health management data warehouse; The visualization display sub-unit is used to retrieve real-time status data from the database and present it visually.

[0052] In one embodiment, the alarm management unit includes: The instrumentation and alarm subunit is configured to store and query historical alarm information from the DCS system. The process alarm subunit is configured to store and query historical process alarm information related to DCS health management services.

[0053] In one embodiment, the DCS health assessment unit includes: The qualitative evaluation subunit is used for qualitative health evaluation based on an expert rule base. The quantitative evaluation subunit is used for quantitative health evaluation based on numerical analysis models. The predictive analysis subunit is used to predict the development trend of the system's health status by combining time series data.

[0054] In one embodiment, the card lifespan management unit includes: The short-term lifetime prediction subunit is used for degradation analysis based on real-time operational data. Long-term life prediction sub-unit, used for life assessment based on stress-intensity model; The operation and maintenance decision support subunit is used to generate maintenance recommendations based on lifetime prediction results.

[0055] In one embodiment, the health evaluation algorithm in the DCS health evaluation unit includes at least one of a fuzzy comprehensive evaluation algorithm and a neural network prediction algorithm.

[0056] In one embodiment, the lifespan prediction algorithm in the card lifespan management unit includes at least one of the Weibull distribution model and the Markov chain model.

[0057] In this embodiment, the simulation operation and risk analysis are geared towards the DCS operation and maintenance activities of nuclear power plants. Based on the DCS parallel system, it combines simulation technology, instrumentation and control technology, digitalization and intelligent technology to realize a series of functions including signal flow diagrams, fault inference, change verification, instrumentation and control equipment classification tools, event tracing and dynamic logic diagrams.

[0058] Signal flow diagrams are based on DCS parallel systems. They analyze the DCS configuration logic, incorporate unit operating data, perform configuration calculations, and present the current logical state to users in the form of a visual logic diagram. This assists maintenance personnel in activities such as logic status monitoring, signal analysis, and logic tracing.

[0059] Fault simulation is based on the DCS parallel system. Through instrumentation and control system and process simulation model, it can simulate DCS faults, interpret the process and impact of the event, and thus provide direction and guidance for the maintenance of nuclear power DCS system.

[0060] Change verification is based on the development of the DCS parallel system. It utilizes the acquired DCS change plan and data of the unit to carry out the modification and implementation in the DCS parallel system. Then, the DCS parallel system is used to verify and analyze the operating status, risks and consequences after the change, which helps operation and maintenance personnel to formulate and improve change plans.

[0061] The instrumentation and control equipment classification tool is based on a parallel system. By introducing equipment failures within the DCS cabinet, it monitors the response of the DCS parallel system and determines the criticality level of the equipment within the DCS cabinet according to the preset criticality classification rules of the instrumentation and control system equipment and the response consequences of the DCS parallel system.

[0062] Event tracing includes initial signal tracing and event cause tracing. Initial signal tracing involves identifying and locating the initial signal of transient dynamics (such as reactor shutdown), dedicated actions (safe injection, press-to-dry), and important equipment actions (such as reactor coolant trip). Event cause tracing involves constructing a fault tracing model using knowledge models combined with reasoning algorithms to analyze and diagnose typical equipment failures that lead to the event.

[0063] Dynamic logic diagrams are generated by analyzing the DCS system engineering configuration files of nuclear power plants and parsing them to produce graphical logic configurations, including signal points and upstream and downstream topological relationships. Based on the DCS parallel system, an instrumentation and control system model is encapsulated to provide dynamic operational data for the graphical logic configuration. Simultaneously, the system can collect PI system calculation data from the DCS health management data warehouse and inject it into the encapsulated instrumentation and control system model to display current unit operating data.

[0064] In one embodiment, the simulation execution and risk analysis module includes: The signal flow diagram unit is used to parse the DCS configuration logic and access the unit operation data to generate a visual logic diagram to display the current logic status. The fault simulation unit is used to simulate DCS faults through instrumentation and control system models and process simulation models, and to deduce the event process and impact. The change verification unit is used to implement change plans in the DCS parallel system and verify the operational status and risk consequences after the change. The instrumentation and control equipment classification tool unit determines the criticality level of equipment based on equipment fault injection and response monitoring, combined with preset classification rules. The event tracing unit is used to locate the source of the initial signal of a transient event by tracing the initial signal, and to analyze the cause of the event by using a fault tracing model. The dynamic logic diagram unit is used to parse the DCS configuration file to generate a graphical logic topology and integrate dynamic operation data with PI system calculation data.

[0065] In one embodiment, the signal flow graph unit specifically includes: DCS configuration logic parsing subunit is used to extract logic operation rules; The real-time data access subunit connects to the unit's operating database to obtain input data; The visualization engine subunit is used to render the results of logical operations into dynamic logic diagrams, supporting logic state monitoring, signal tracing, and anomaly location.

[0066] In one embodiment, the fault deduction unit includes: The fault scenario configuration subunit is used to set the DCS fault type and parameters; Multi-model coupling subunit is used to synchronously drive the dynamic interaction between the instrumentation and control system model and the process simulation model; The impact assessment subunit is used to generate reports on fault propagation paths and consequences, providing quantitative basis for maintenance strategies.

[0067] In one embodiment, the change verification unit includes: The change plan import sub-unit is used to receive DCS transformation plans and configuration files; Virtual implementation subunits are used to deploy change logic in a DCS parallel system; The risk simulation sub-unit predicts operational risks after changes based on historical data and simulation models, and outputs verification results and optimization suggestions.

[0068] In one embodiment, the event sourcing unit includes: The initial signal tracing subunit is used to locate the initial signal source of a transient event through time series analysis and signal correlation matching; The fault tracing model subunit, based on knowledge graphs and reasoning algorithms, reverse-engineers the equipment fault chain and outputs diagnostic conclusions and root causes of the fault.

[0069] In one embodiment, the dynamic logic diagram unit includes: The configuration file parsing subunit is used to extract signal points and topological relationships and generate a logic diagram framework. The data encapsulation subunit is used to dynamically bind the operating data of the DCS parallel system and the PI system data to the logic diagram; The visual interaction sub-unit is used to support logical layer-level navigation, real-time data refresh, and historical data playback.

[0070] In this embodiment, maintenance management is geared towards the DCS operation and maintenance activities of nuclear power plants. It manages the processes, process documents, guidelines and procedures, maintenance data, etc. during the operation and maintenance process, and realizes full process control of maintenance actions in the instrumentation and control field from planning, preparation, implementation and shutdown.

[0071] Maintenance plan management provides information display of maintenance activities, including push display of maintenance plans, maintenance data statistics, and work order overdue warnings and upgrades, helping instrumentation and control personnel to clarify their work tasks.

[0072] The tool and equipment management section manages the master data of tools and equipment materials, providing a data query and display window and supporting reminders for overdue borrowing and return. Through functions such as tool and equipment classification, management, and maintenance, it enables comprehensive control over the tool and equipment process, thereby improving the efficiency of tool and equipment use and management.

[0073] The maintenance document management system enables functions such as archiving, classifying, querying, reviewing, and reminding users of maintenance documents, thereby improving the efficiency of document usage and management. It also features periodic review capabilities to ensure the validity and security of maintenance documents.

[0074] Maintenance man-hour statistics track the man-hours used by instrumentation and control personnel in maintenance activities, providing chart and data display. It enables various functions such as recording, querying, calculating, and statistically analyzing maintenance man-hours, thereby achieving refined management of maintenance man-hours and serving as a basis for personnel maintenance work records and performance evaluation.

[0075] The authorization management module is specifically designed to manage the authorization, permissions, and roles of instrumentation and control personnel, establishing an authorization management system that matches permissions with maintenance activities. It enables functions such as hierarchical permission configuration, personnel authorization, technical authorization identification, and authorization expiration warnings. Administrators can easily perform granular authorization management of organizational structures, personnel, and resources, ensuring information security within the organization.

[0076] Intelligent maintenance digitizes maintenance procedures and develops wearable devices based on AR technology to achieve automatic guidance of maintenance procedures, automatic prompts during on-site inspections, step guidance, operation error correction, and automatic process monitoring, thus realizing intelligent maintenance of DCS cabinets on-site.

[0077] In one embodiment, the maintenance management module includes: The maintenance plan management unit is used for full-process control of DCS operation and maintenance activities in nuclear power plants. By integrating the maintenance plan push and display interface, work order timeliness monitoring module and data statistics engine, it realizes dynamic information display of maintenance activities, work order overdue warning prompts and multi-dimensional maintenance data statistical analysis. The tool and equipment management unit is used to build a master database of tools and equipment materials, provide a fast search interface based on classification codes and a borrowing status tracking module, realize the full life cycle management of tools and equipment in the warehouse registration, usage tracking and maintenance records, and configure an overdue return early warning mechanism. The maintenance document management unit is used for standardized archiving management of maintenance documents. It integrates a document classification index engine, version control module and automatic review reminder system, supports online approval process management, and has a periodic review triggering mechanism based on document type and validity verification function. The maintenance man-hour statistics unit is used to collect man-hour data of instrumentation and control personnel participating in maintenance activities. It generates statistical reports through the man-hour input interface and calculation model library, provides multi-dimensional charts based on projects, personnel and work orders, and establishes a data connection channel with the performance appraisal system. The authorization management unit is used to build the RBAC permission management system. It includes a role permission matrix configurator, an authorization period management module, and a security audit log system. It enables hierarchical permission configuration, technical authorization identification and matching, and authorization certificate expiration warning. It supports fine-grained permission allocation at the organizational level. The intelligent maintenance unit is used to transform maintenance procedures into structured electronic work orders. Based on an AR visualization engine and wearable device interaction interface, it realizes automatic guidance of maintenance steps, real-time verification of operation compliance, and process image traceability. It is also equipped with operation error correction algorithms and automatic monitoring functions for DCS cabinet maintenance.

[0078] The DCS Operation and Maintenance Big Data Model is built upon the DCS health management data warehouse and advanced big data modeling technology. It constructs an expert knowledge base platform that supports knowledge acquisition, analysis, management, and finally, push and application. This enables a comprehensive and intelligent auxiliary upgrade of the nuclear power DCS system to meet the nuclear power industry's needs for efficient and safe operation management. The DCS Operation and Maintenance Big Data Model includes knowledge base management, intelligent dialogue, office assistant, data services, and operation and maintenance assistant.

[0079] Knowledge base management involves storing and managing a large amount of historical data and documents from nuclear power plants. This includes structured data such as maintenance plans, training documents, vendor documents, equipment manuals and equipment information, and experience feedback.

[0080] Intelligent dialogue includes intelligent retrieval and intelligent question answering. Intelligent retrieval allows users to input query conditions in natural language, and the DCS operation and maintenance big data model will automatically understand the user's intent and retrieve relevant information from the knowledge base. Intelligent question answering allows users to input query conditions in natural language, and the system will automatically retrieve relevant information from the knowledge base, reorganize it into a continuous and smooth natural language output answer, and append a ranking of the original text based on the search relevance.

[0081] The office assistant includes document generation and document interpretation. Document generation understands the topic and content based on the user's needs and input data, and then uses natural language processing technology to generate text that conforms to grammar and semantics. Document interpretation is a large model that can quickly interpret various types of documents, such as technical documents, policy documents, and operating specifications.

[0082] Data services include data query and data visualization. Data query allows users to input query conditions in natural language, enabling them to quickly find the information they need from a large amount of power plant operation data and quickly locate the relevant location. Data visualization can be used to automatically generate data visualizations from large models, and the appropriate chart type (such as bar chart, line chart, scatter plot) is selected by understanding the data characteristics and user needs.

[0083] The operations and maintenance assistant includes status handling push notifications, automatic work order generation, and automatic experience feedback generation. Status handling push notifications are based on a large language model, utilizing knowledge, experience, rules, and algorithms to possess observation, reasoning, planning, and decision-making capabilities similar to those of operations and maintenance personnel. When an alarm occurs in the DCS system, fault analysis can be performed based on the alarm list, and handling suggestions can be pushed. Automatic work order generation involves the large model completing fault reasoning and then learning the structure and writing of maintenance work orders to automatically generate work orders. Experience feedback generation involves the automatic generation of experience feedback (status reports, corrective actions, event reports, etc.) after operations and maintenance personnel complete work order tasks, based on work order information and completion reports, and by learning the structure and writing of experience feedback.

[0084] In one embodiment, the DCS operation and maintenance large model module includes: The knowledge base management unit is used to build and maintain a dedicated knowledge repository for nuclear power DCS systems. It uses a structured storage engine to classify and manage historical data and documents of nuclear power plants, including maintenance plans, training documents, vendor documents, equipment manuals, equipment information and experience feedback data, and provides digital archiving, version control and multi-dimensional search support. The intelligent dialogue unit provides natural language interaction services, enabling users to achieve intelligent retrieval and question-answering functions through semantic understanding and intent recognition technologies. Intelligent retrieval is based on knowledge graphs to associate multimodal data and return a list of original documents ranked by relevance. Intelligent question answering uses a large language model to generate multi-document summaries of the retrieval results, outputs logically coherent answers, and includes original text citations and confidence assessments. The Office Assistant unit is used to assist in the operation and maintenance office scenarios of nuclear power DCS. It automatically generates technical documents and reports that conform to industry standards through natural language generation technology (NLG), and realizes semantic interpretation and key information extraction of unstructured documents such as technical specifications and policy documents based on a multi-format parsing engine. The data service unit is used to provide intelligent services for the operation data of nuclear power DCS system. It supports the rapid location of massive data such as equipment status and operating parameters through natural language query interface, and automatically generates interactive visualization charts such as bar charts and trend curves through data feature analysis algorithms to achieve data insight and abnormal pattern recognition. The Operation and Maintenance Assistant unit is used to realize the intelligent operation and maintenance of the entire nuclear power DCS system. This includes fault reasoning and handling suggestions based on alarm information, automatic generation of work orders combined with maintenance work order templates, and automatic generation of experience feedback reports based on work order execution data, forming a closed-loop knowledge update mechanism that includes fault mode analysis, handling process recording, and summary of preventive measures.

[0085] Equipment basic information management involves collecting and managing basic information data related to the operation and maintenance of the nuclear power plant's DCS. This provides a foundation for the management and supervision of nuclear power plant DCS equipment and, based on the collected data, provides operation and maintenance personnel with data statistical analysis, spare parts prediction, and outline optimization functions. It mainly includes functions such as equipment management, spare parts management, preventive maintenance management, and periodic test management.

[0086] The device management system manages the specific information of devices at levels 0, 1, and 2 of the DCS system using a unified data format, and has management processes such as adding, deleting, querying, modifying, and exporting.

[0087] Spare parts management is used to manage and display information such as spare parts model, category, quantity, warranty level, master data, lifespan, scrap date, and images. It also supports spare parts tree structure management and has functions such as quick query and fuzzy search.

[0088] Preventive maintenance management includes preventive maintenance program management and preventive maintenance project management. Preventive maintenance program management involves regularly synchronizing with the ERM system through a data interface to dynamically collect power plant preventive maintenance programs, displaying statistical results and dynamic change details of program items. Preventive maintenance projects, also regularly synchronized with the ERM system through a data interface, compare equipment pre-maintenance projects and cycles based on DCS equipment preventive maintenance projects, providing difference analysis and effectiveness evaluation of preventive maintenance projects.

[0089] Regular test management involves periodically synchronizing with the ASP-1 system through a data interface to dynamically collect the power plant's regular test outlines, display the statistical results of the outline items, and details of dynamic changes.

[0090] In one embodiment, the device basic information management module includes: The equipment management unit is used to build a database of the entire life cycle of equipment in the nuclear power DCS system. It uses a data standardization engine to uniformly model the basic information of equipment at levels 0, 1, and 2, supports the functions of adding, deleting, multi-dimensional composite querying, dynamic modification, and batch exporting of equipment parameters, and establishes a visual mapping model of equipment hierarchical topology. The spare parts management unit is used to realize the holographic file management of spare parts in the nuclear power DCS system. Based on multi-dimensional attribute modeling technology, it stores key parameters such as spare parts model, service life, quality assurance level, and master data code in a structured manner. It supports the dynamic maintenance of the spare parts tree classification architecture and provides spare parts map matching and cross-field fuzzy search functions based on image recognition. The preventive maintenance management unit is used to synchronize the preventive maintenance outline in real time, establish a pre-maintenance cycle optimization model based on the characteristics of DCS equipment, and output a pre-maintenance project execution effectiveness evaluation report and maintenance strategy adjustment suggestions. The periodic test management unit is used to establish a periodic test intelligent monitoring platform, realize the automatic synchronization of test outlines through the ASP-1 system data interface, generate a panoramic view of the execution progress of test items based on time series analysis algorithms, and build a test data deviation early warning model to identify abnormal fluctuation trends. The Equipment Basic Information Management Module is used to integrate the nuclear power DCS system operation and maintenance basic data resource pool. Through the fusion of multi-source data such as equipment management, spare parts management, preventive maintenance management and periodic test management, it provides equipment failure mode statistical analysis, spare parts demand prediction models and maintenance outline optimization schemes, forming a decision support capability covering the entire life cycle of equipment.

[0091] In one embodiment, the preventive maintenance management unit includes: The outline management subunit realizes real-time synchronization of preventive maintenance outlines through the ERMs system data interface, and dynamically generates outline item compliance statistical charts and version iteration difference heatmaps. The project management sub-unit establishes a pre-maintenance cycle optimization model based on the characteristics of DCS equipment. Through multi-factor correlation analysis of equipment health and maintenance records, it outputs a pre-maintenance project execution effectiveness evaluation report and maintenance strategy optimization suggestions.

[0092] In one embodiment, the configuration management module controls the changes and management of DCS configuration items in a nuclear power plant, ensuring the integrity, consistency, and traceability of data, files, knowledge, and processes generated or required during operation and maintenance. It is a crucial tool for achieving effective equipment management and maintenance. The configuration management module includes: The setting management unit is used for unified information management and maintenance of instrumentation and control system parameter settings, including upper and lower limits of quality positions, default values, power failure / failure safety status, etc., and supports pushing relevant data such as equipment and historical modification records to other business modules to enhance the data interoperability of parameter setting query. The temporary enforcement management unit is used to manage the power plant's temporary enforcement documents and processes, monitor the signal status of DCS enforcement points, assist the power plant in completing temporary enforcement work, and achieve full-process status control of temporary enforcement. The change management unit is used to manage power plant change documents. In conjunction with the DCS parallel system, it links with the change verification module in the simulation operation and risk analysis system to assist in the power plant change verification work and realize the calculation of probability risk indicators for changes.

[0093] The Equipment Document Management Unit manages documents related to instrumentation and control equipment in nuclear power plants, including Level 0 equipment EOMMs, system manuals (FD diagrams, SAMA diagrams, IO lists, setting manuals, termination tables, cable routing diagrams, etc.), and manufacturer documents (configuration diagrams, wiring diagrams, etc.). It establishes and reflects file relationships through file metadata attributes that provide file association fields. When a user views a file, providing upstream or downstream file jump entries allows redirection to the location of the upstream or downstream file. When implementing the jump function, strict access control is required to ensure users have permission to access the target file. If the upstream or downstream file of a file changes, the Equipment Document Management module immediately updates the corresponding metadata attributes.

[0094] The model and service layer provides underlying model and service capabilities support for the DCS health management platform. It is based on the DCS parallel system, the DCS large model, and algorithm services, with the DCS health management data warehouse as the underlying data support. In this embodiment, the model and service layer is designed to include the DCS parallel system, the DCS large model module, the algorithm service module, and the DCS health management data warehouse.

[0095] The DCS parallel system is one of the computing engines of the DCS health management platform. It is designed to meet the needs of DCS operation and maintenance, and builds a complete mapping environment for the reference unit's DCS software / hardware system and equipment. It has functions such as simulation analysis, maintenance support and skills training in the DCS operation and maintenance process, and supports DCS simulation analysis verification, maintenance risk analysis verification, fault simulation, and instrumentation and control equipment classification.

[0096] DCS large model is a large language model technology based on retrieval enhancement and generation. It combines the advantages of retrieval and generation to significantly improve the knowledge coverage, accuracy, timeliness and interpretability in domain applications, while reducing the dependence on a large amount of labeled data.

[0097] The algorithm service is used for DCS health assessment algorithms, card lifespan measurement algorithms, and large model algorithms.

[0098] The DCS health management data warehouse is built by researching and analyzing multi-source heterogeneous data, including DCS system data, equipment basic information, status data, and maintenance data. It provides data support for various application modules of DCS health management. It possesses functions such as DCS data acquisition, data storage, data processing, data services, and business services, facilitating the management, analysis, and use of data in DCS applications.

[0099] In one embodiment, the model and service layer includes: The DCS parallel system is used to build a DCS hardware and software mapping environment to support operation and maintenance computing needs such as simulation verification, maintenance risk analysis, fault simulation and equipment classification. The DCS parallel system builds a complete mapping environment for the reference unit's DCS hardware and software system and equipment, providing functions such as simulation analysis, maintenance support, and skills training, and supports computing needs such as simulation analysis verification, maintenance risk analysis verification, fault simulation and instrumentation and control equipment classification in DCS operation and maintenance scenarios. The DCS large model module is used to improve the knowledge coverage, accuracy, and interpretability of the DCS domain based on retrieval-enhanced generation (RAG) technology, and reduce the dependence on labeled data; the large language model technology based on retrieval-enhanced generation combines the advantages of retrieval and generation to improve the knowledge coverage, accuracy, timeliness, and interpretability of DCS domain applications, while reducing the dependence on large-scale labeled data. The algorithm service module provides health assessment, card life prediction, and large-scale model algorithms to support the platform's intelligent analysis and decision-making functions. The algorithm service module provides DCS health assessment algorithms, card life prediction algorithms, and large-scale model algorithm support to support various intelligent analysis and decision-making functions of the platform. The DCS health management data warehouse is used to integrate multi-source heterogeneous data, providing collection, storage and processing services to support the data needs of various application modules. The DCS health management data warehouse integrates multi-source heterogeneous data such as DCS system data, equipment basic information, status data, and maintenance data, providing data collection, storage, processing and service functions, and providing a unified data support platform for various application modules of DCS health management.

[0100] Throughout the entire lifecycle, the operations and maintenance (O&M) deployment layer utilizes an integrated development and operations platform to achieve unified lifecycle management of the nuclear power DCS health management platform, encompassing requirements management, test management, code management, continuous integration, continuous deployment, and system operations and maintenance. The O&M deployment layer provides automated code building and engineering deployment processes, improving software development and deployment efficiency. Simultaneously, it interfaces with third-party platforms, using unified standards to check outputs, ensuring system reliability and stability. The O&M deployment layer includes: The logging subsystem is used to uniformly manage all operational logs of the DCS health management platform, collect logs from various subsystem microservices, and provide quick location and retrieval. The monitoring subsystem is used to monitor and provide early warning of the overall operating indicators of the DCS health management platform. For running servers, middleware, and business services, it can view the service's memory, network throughput, disk and CPU in real time.

[0101] The infrastructure layer provides the hardware support environment, operating system, network security, and resource management for platform development. The infrastructure layer includes cluster deployment, elastic computing services, distributed collaborative services, distributed file systems, security management, open storage services, remote procedure calls, task scheduling services, resource management, network resources, operating system, and cluster monitoring.

[0102] This embodiment details the technical solutions for various sub-applications and business sub-modules of the DCS health management platform. It includes designs for DCS remote status monitoring, DCS health assessment algorithms, card lifespan algorithms, DCS health management data warehouse, DCS health management platform configuration change verification, DCS fault simulation, DCS event tracing, instrumentation and control equipment classification, signal flow diagrams, DCS operation and maintenance large model, and DCS health management multi-power plant cluster management.

[0103] like Figure 4As shown, this embodiment provides a design method for a DCS remote status monitoring module in a DCS health management platform. The DCS health management platform data collector transmits DCS operating status data from the first layer to the second layer network area. Then, the second layer data is transmitted to the power plant management network for storage through the third collector of the DCS health management platform. The DCS health management platform application server will regularly pull the data from the third collector in the management network and store it in the DCS health management data warehouse.

[0104] DCS remote status monitoring receives status monitoring information, historical maintenance data, offline test data, and fault expert knowledge from the DCS health management data warehouse, serving as a comprehensive information source for health monitoring and assessment. Users can monitor information online in real time to analyze and judge the current health status and development trend of the system.

[0105] like Figure 5 As shown, this embodiment provides a DCS health assessment algorithm design method in a DCS health management platform. It obtains DCS system status information through various methods such as DCS online monitoring, offline testing, and historical maintenance data collection. It achieves effective utilization of information through data fusion and establishes a model for DCS system health status assessment and prediction based on the fusion of three models: analytical model, knowledge-based model, and data-based model. This provides a foundation for system fault prediction and health assessment.

[0106] This embodiment provides a card lifespan algorithm design method in a DCS health management platform. Based on the card's structural composition and various characteristic data, and combined with currently common industry-standard lifespan prediction algorithms, it predicts the lifespan of DCS system cards, including short-term and long-term lifespan predictions. The evaluation results are used to assist DCS operation and maintenance. Lifespan prediction is divided into mechanistic models, data-driven models, and mechanistic-data fusion models.

[0107] like Figure 6 As shown, this embodiment provides a design method for a DCS health management data warehouse within a DCS health management platform. Through research and analysis of multi-source heterogeneous data, including DCS system data, equipment basic information, status data, maintenance data, and configuration data, the system design of the DCS health management data warehouse was completed. As the data foundation for various application modules of the DCS health management platform, the DCS health management data warehouse possesses functions such as access to various data sources, collection of various DCS data, data governance, data storage, and data services.

[0108] like Figure 7As shown, this embodiment provides a configuration change verification design method in a DCS health management platform. It is based on a DCS parallel system, which uses the acquired DCS change plan or data of the unit to carry out the modification and implementation in the DCS parallel system. Then, the DCS parallel system is used to verify and analyze the operation status, risks and consequences after the change, which helps operation and maintenance personnel to formulate and improve change plans.

[0109] like Figure 8 As shown, this embodiment provides a DCS fault simulation design method in a DCS health management platform. Fault simulation is based on the DCS parallel system simulation engine to construct scenarios and simulate situations for fault events, simulating the expected response of equipment and assessing the impact of faults. It provides a complete reproduction of the entire event occurrence process, quantitatively describes the coupling characteristics in the event evolution process, and identifies key elements by analyzing fault propagation process data. This helps to find system vulnerabilities and generate experience feedback that is pushed to the DCS expert knowledge base.

[0110] like Figure 9 As shown, this embodiment provides a DCS event tracing design method in a DCS health management platform. Event tracing technology primarily addresses the tracing of the initial event signal and the location of the event's cause during nuclear power plant event analysis. When an event occurs, it assists nuclear power plant maintenance personnel in quickly locating the initial event signal and, combined with a knowledge reasoning model, rapidly analyzing and diagnosing the fault points that led to the event, reducing the workload of manual analysis and improving the efficiency and accuracy of event analysis.

[0111] like Figure 10 As shown, this embodiment provides a design method for a classification tool for instrumentation and control equipment in a DCS health management platform. By analyzing the failure modes of instrumentation and control system equipment, and based on the DCS parallel system, it deduces the consequences of various failure modes, determines the equipment level, and forms an automated classification process.

[0112] This embodiment provides a signal flow diagram design method in a DCS health management platform. It analyzes the DCS configuration logic, connects the unit operation data, performs configuration calculations, and displays the current logic status to the user in the form of a visual logic diagram. This can assist operation and maintenance personnel in performing activities such as logic status monitoring, signal analysis, and logic tracing.

[0113] like Figure 11As shown in this embodiment, a DCS operation and maintenance large model design method is provided in the DCS health management platform. Based on DCS data warehouse and large language model technology, it constructs an intelligent operation and maintenance assistant system from knowledge acquisition, analysis and management, to final push and application, serving the actual work of nuclear power plants. It possesses functions such as intelligent retrieval, intelligent question answering, data visualization, intelligent file parsing and generation, work order generation, and operation and maintenance assistant, reducing the workload of operation and maintenance personnel and improving overall operation and maintenance capabilities.

[0114] like Figure 12 As shown, this embodiment provides a design method for DCS health management of multiple nuclear power plants within a DCS health management platform. Multi-plant cluster management technology is a software architecture based on multi-tenancy, enabling multiple nuclear power plants to share a single platform service and achieving data and behavior isolation between them. The DCS health management platform is a SaaS application deployed on a cloud platform. Through multi-plant cluster management technology, it allows multiple nuclear power plants to share a single DCS health management platform while ensuring data isolation between them. Compared to traditional software models, it reduces deployment time, saves manpower and hardware resource costs in terms of operation and maintenance; and in terms of architecture design, it achieves "high cohesion and low coupling," enabling efficient aggregation and secure isolation of data for each power plant tenant.

[0115] The above description is only a specific embodiment of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application.

Claims

1. A DCS health management platform based on agile development and a distributed architecture, characterized in that, include: The display layer is used to monitor the operation and maintenance status of the DCS, and to provide early warnings and analysis of abnormal key indicators. The business application layer is used for intelligent operation and maintenance management of nuclear power plant DCS systems, remote monitoring and life prediction of software and hardware status, construction of dynamic logic deduction and fault tracing mechanism, full-process digital operation control, overall maintenance plan, access audit and intelligent guidance, and establishment of a full life cycle data system. The model and service layer is used to sort out and analyze the operation and maintenance data related to the DCS system in nuclear power plants, and to build DCS parallel system, DCS large model and DCS health evaluation model to provide support for DCS simulation operation and risk analysis, DCS operation and maintenance management and DCS health evaluation. The operations and maintenance deployment layer is used for integrated management throughout the entire lifecycle, managing all operational logs of the DCS health management platform and monitoring and alerting operational indicators. The infrastructure layer provides hardware support, operating systems, network security, and resource management.

2. The DCS health management platform based on agile development and a distributed architecture as described in claim 1, characterized in that, The business application layer includes: The equipment health monitoring module is used to remotely monitor the hardware, software and network status of the DCS system in real time, store and manage instrumentation and process alarm data, perform qualitative and quantitative health analysis on the overall system and its parts, and predict the short and long lifespan of the components. The simulation operation and risk analysis module is used to parse the DCS configuration logic to generate a visual dynamic logic diagram, simulate equipment failure to deduce the impact path of events, verify the risk consequences of change plans, evaluate the criticality level of instrumentation and control equipment through fault injection, and trace the initial signal source and root cause of transient events. The maintenance management module is used to coordinate the entire equipment maintenance process, and integrates functions such as maintenance plan push and display, work order overdue warning and data statistical analysis. The DCS operation and maintenance large model module is used for the digital management and control of the entire DCS operation and maintenance process in nuclear power plants, enabling operation compliance verification and image traceability. The equipment basic information management module is used to build a data management system for the entire life cycle of nuclear power DCS system equipment and integrate multi-source data resources. The configuration management module is used to control the changes and management of DCS configuration items in nuclear power plants.

3. The DCS health management platform based on agile development and a distributed architecture as described in claim 2, characterized in that, The equipment health monitoring module includes: The DCS status monitoring unit is used to remotely monitor and troubleshoot DCS system hardware, software and network through data acquisition, storage and visualization. The alarm management unit is used to classify, store, and query instrumentation alarm information and process alarm information from the DCS system. The DCS health assessment unit is used to combine feature data and health assessment algorithms to perform qualitative and quantitative health status assessment and prediction of the DCS system. The card life management unit is used to generate short-term and long-term life prediction results based on card feature data and life prediction algorithms to support operation and maintenance decisions.

4. The DCS health management platform based on agile development and a distributed architecture as described in claim 3, characterized in that, The DCS status monitoring unit includes: The communication interface subunit is used to collect monitoring data from the DCS system. The data processing subunit is used to process the collected data and store it in the DCS health management data warehouse; The visualization display sub-unit is used to retrieve real-time status data from the database and present it visually.

5. The DCS health management platform based on agile development and a distributed architecture as described in claim 3, characterized in that, The alarm management unit includes: The instrument control alarm subunit is used to store and query historical alarm information of the DCS system; The process alarm subunit is used to store and query historical process alarm information related to DCS health management operations.

6. The DCS health management platform based on agile development and a distributed architecture as described in claim 3, characterized in that, The DCS health assessment unit includes: The qualitative evaluation subunit is used for qualitative health evaluation based on an expert rule base. The quantitative evaluation subunit is used for quantitative health evaluation based on numerical analysis models. The predictive analysis subunit is used to predict the development trend of the system's health status by combining time series data.

7. The DCS health management platform based on agile development and a distributed architecture as described in claim 3, characterized in that, The card life management unit includes: The short-term lifetime prediction subunit is used for degradation analysis based on real-time operational data. Long-term life prediction sub-unit, used for life assessment based on stress-intensity model; The operation and maintenance decision support subunit is used to generate maintenance recommendations based on lifetime prediction results.

8. The DCS health management platform based on agile development and a distributed architecture as described in claim 2, characterized in that, The simulation operation and risk analysis module includes: The signal flow diagram unit is used to parse the DCS configuration logic and access the unit operation data to generate a visual logic diagram to display the current logic status. The fault simulation unit is used to simulate DCS faults through instrumentation and control system models and process simulation models, and to deduce the event process and impact. The change verification unit is used to implement change plans in the DCS parallel system and verify the operational status and risk consequences after the change. The instrumentation and control equipment classification tool unit is used to determine the criticality level of equipment based on equipment fault injection and response monitoring, combined with preset classification rules. The event tracing unit is used to locate the source of the initial signal of a transient event by tracing the initial signal, and to analyze the cause of the event by using a fault tracing model. The dynamic logic diagram unit is used to parse the DCS configuration file to generate a graphical logic topology and integrate dynamic operation data with PI system calculation data.

9. The DCS health management platform based on agile development and a distributed architecture as described in claim 8, characterized in that, The signal flow graph unit includes: DCS configuration logic parsing subunit is used to extract logic operation rules; The real-time data access subunit connects to the unit's operating database to obtain input data; The visualization engine subunit is used to render the results of logical operations into dynamic logic diagrams, supporting logic state monitoring, signal tracing, and anomaly location.

10. The DCS health management platform based on agile development and a distributed architecture as described in claim 8, characterized in that, The fault simulation unit includes: The fault scenario configuration subunit is used to set the DCS fault type and parameters; Multi-model coupling subunit is used to synchronously drive the dynamic interaction between the instrumentation and control system model and the process simulation model; The impact assessment subunit is used to generate reports on fault propagation paths and consequences, providing quantitative basis for maintenance strategies.

11. The DCS health management platform based on agile development and a distributed architecture as described in claim 8, characterized in that, The change verification unit includes: The change plan import sub-unit is used to receive DCS transformation plans and configuration files; Virtual implementation subunits are used to deploy change logic in a DCS parallel system; The risk simulation subunit is used to predict operational risks after changes based on historical data and simulation models, and output verification results and optimization suggestions.

12. The DCS health management platform based on agile development and a distributed architecture as described in claim 8, characterized in that, The event tracing unit includes: The initial signal tracing subunit is used to locate the initial signal source of a transient event through time series analysis and signal correlation matching; The fault tracing model subunit is used to reverse-engineer the equipment fault chain based on knowledge graphs and reasoning algorithms, and output diagnostic conclusions and root causes of the fault.

13. The DCS health management platform based on agile development and a distributed architecture as described in claim 8, characterized in that, The dynamic logic diagram unit includes: The configuration file parsing subunit is used to extract signal points and topological relationships and generate a logic diagram framework. The data encapsulation subunit is used to dynamically bind the operating data of the DCS parallel system and the PI system data to the logic diagram; The visual interaction sub-unit is used to support logical layer-level navigation, real-time data refresh, and historical data playback.

14. The DCS health management platform based on agile development and a distributed architecture as described in claim 2, characterized in that, The maintenance management module includes: The maintenance planning management unit is used for full-process control of DCS operation and maintenance activities in nuclear power plants, enabling dynamic information display of maintenance activities, early warning of overdue work orders, and multi-dimensional statistical analysis of maintenance data. The tool and equipment management unit is used to build a master database of tools and equipment materials, realize the full life cycle management of tools and equipment in terms of warehousing registration, usage tracking, and maintenance records, and configure an early warning mechanism for overdue return; The maintenance document management unit is used for standardized archiving management of maintenance documents. It integrates a document classification index engine, version control module and automatic review reminder system, supports online approval process management, and has a periodic review triggering mechanism based on document type and validity verification function. The maintenance time statistics unit is used to collect time data, generate statistical reports, provide multi-dimensional charts and graphs, and establish a data interface with the performance appraisal system. The authorization management unit is used to build the RBAC permission management system, realize hierarchical permission configuration, technical authorization identification and matching, and authorization certificate expiration warning, and support fine-grained permission allocation at the organizational level; The intelligent maintenance unit is used to transform maintenance procedures into structured electronic work orders, enabling automatic guidance of maintenance steps, real-time verification of operational compliance, and process image-based traceability.

15. The DCS health management platform based on agile development and a distributed architecture as described in claim 2, characterized in that, The DCS operation and maintenance large model module includes: The knowledge base management unit is used to build and maintain a dedicated knowledge repository for nuclear power DCS systems, and to classify and manage historical data and documents of nuclear power plants through a structured storage engine. The intelligent dialogue unit is used to provide natural language interaction services, supporting users to achieve intelligent retrieval and question-and-answer functions through semantic understanding and intent recognition technologies; The Office Assistant unit is used to assist in the operation and maintenance office scenarios of nuclear power DCS, automatically generating technical documents and reports that conform to industry standards, and performing semantic interpretation and key information extraction of unstructured documents; The data service unit is used to provide intelligent services for the operation data of nuclear power DCS systems, automatically generate interactive visualization charts, and realize data insights and anomaly pattern recognition. The Operation and Maintenance Assistant unit is used to realize the intelligent operation and maintenance of the entire nuclear power DCS system, forming a closed-loop knowledge update mechanism for fault mode analysis, handling process recording and preventive measures summary.

16. The DCS health management platform based on agile development and a distributed architecture as described in claim 2, characterized in that, The equipment basic information management module includes: The equipment management unit is used to build a database of the entire life cycle of nuclear power DCS system equipment. It uses a data standardization engine to uniformly model the basic information of equipment at levels 0, 1, and 2, and establishes a visual mapping model of equipment hierarchical topology. The spare parts management unit is used to realize the holographic file management of spare parts in the nuclear power DCS system, perform structured storage, support dynamic maintenance of the spare parts tree classification architecture, and provide spare parts map matching and cross-field fuzzy search functions based on image recognition. The preventive maintenance management unit is used to synchronize the preventive maintenance outline in real time, establish a pre-maintenance cycle optimization model based on the characteristics of DCS equipment, and output a pre-maintenance project execution effectiveness evaluation report and maintenance strategy adjustment suggestions. The periodic test management unit is used to establish a periodic test intelligent monitoring platform, realize the automatic synchronization of test outlines, and build a test data deviation early warning model to identify abnormal fluctuation trends. The equipment basic information management module is used to integrate the nuclear power DCS system operation and maintenance basic data resource pool, and provide equipment failure mode statistical analysis, spare parts demand prediction model and maintenance outline optimization scheme.

17. The DCS health management platform based on agile development and a distributed architecture as described in claim 16, characterized in that, The preventive maintenance management unit includes: The outline management subunit is used for real-time synchronization of preventive maintenance outlines, dynamically generating statistical charts of outline item compliance and heat maps of version iteration differences; The project management sub-unit is used to output a pre-maintenance project execution effectiveness assessment report and maintenance strategy optimization suggestions through multi-factor correlation analysis of equipment health and maintenance records.

18. The DCS health management platform based on agile development and a distributed architecture as described in claim 2, characterized in that, The configuration management module includes: The device file management unit is used for device file uploading, version control, structured storage, and fast retrieval; The setting management unit is used for unified information management and maintenance of the parameter setting values ​​of the instrumentation and control system; The temporary mandatory management unit is used to manage the power plant's temporary mandatory documents and processes, monitor the signal status of DCS mandatory points, and assist the power plant in completing temporary mandatory work. The change management unit is used to manage power plant change documents. In conjunction with the DCS parallel system, it links with the change verification module in the simulation operation and risk analysis system to assist in the power plant change verification work and realize the calculation of probability risk indicators for changes.

19. The DCS health management platform based on agile development and a distributed architecture as described in claim 1, characterized in that, The model and service layer includes: The DCS parallel system is used to build a complete mapping environment for the reference unit's DCS hardware and software systems and equipment, and to realize the fault simulation function for various DCS equipment. The DCS large model module is used to improve the coverage, accuracy and interpretability of DCS domain knowledge and reduce dependence on labeled data; The algorithm service module provides health assessment, card life prediction, and large model algorithms to support the platform's intelligent analysis and decision-making functions. The DCS health management data warehouse is used to integrate multi-source heterogeneous data, provide collection, storage and processing services, support the data needs of various application modules, and provide a unified data support platform for various application modules of DCS health management.

20. The DCS health management platform based on agile development and a distributed architecture as described in claim 1, characterized in that, The operations and maintenance deployment layer includes: The logging subsystem is used to uniformly manage all operational logs of the DCS health management platform and collect logs from various subsystem microservices. The monitoring subsystem is used to monitor and provide early warning of the overall operating indicators of the DCS health management platform, and to view the memory, network throughput, disk and CPU of running servers, middleware and business services in real time.

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