Digital power plant equipment full life cycle management method

Abstract

The invention relates to the technical field of power plant equipment management, and discloses a digital power plant equipment full life cycle management method comprising the following steps: S1, equipment data digitalization; s2, intelligent point inspection and operation and maintenance; s3, data acquisition and integration; s4, performing causal analysis and index management; s5, intelligent decision making and optimization are carried out; the digital power plant equipment full-life-cycle management method can cover the whole process of planning, purchasing, installing, operating, maintaining and decommissioning of power plant equipment, breaks information islands of all stages, achieves integration and sharing of data, reduces shutdown loss caused by sudden failures through preventive maintenance and predictive analysis, and improves the safety of power plant equipment. The maintenance cost is effectively reduced, the equipment management process is optimized, human errors are reduced, the management efficiency is improved, and the maximum utilization of resources is realized and potential problems of the equipment are found in advance by digitally evaluating the residual benefits of the equipment and formulating a reasonable decommissioning plan.

Description

Technical Field

[0001] The present invention relates to the technical field of power plant equipment management, and particularly to a digital power plant equipment full-life cycle management method. Background Art

[0002] Power plants are one of the infrastructure of modern society, providing necessary electricity for all walks of life. Power plant equipment is the infrastructure of a power plant. The reliability and efficiency of power plant equipment are directly related to the stability of power supply. With the development of the times and technology, traditional power plants are beginning to transform into digital power plants. A digital power plant refers to a theory and method that quantifies, analyzes, controls, and makes decisions on the full life cycle of physical objects and working objects in a power plant to enhance the overall value of the power plant. It is not just a simple digital system integration, but covers the whole process from the design, construction to operation and maintenance of the power plant, emphasizing the realization of the safe, efficient, and environmentally friendly operation of the power plant through information technology, intelligent control, and management decision-making technology.

[0003] At present, a large number of equipment are used during the operation of power plants, and these equipment require equipment full-life cycle management. Some steps of traditional equipment full-life cycle management need to be sorted out manually, relying strongly on manual work, and there are a series of disadvantages. For example, due to a large amount of messy and unrelated data, inconsistent numbering, and the possible loss of paper plans and other factors, the relationships between equipment ledgers, operation data, maintenance data, etc. have not been deeply analyzed and mined. Power plants cannot effectively predict the reliability, operation years, and maintenance plans of equipment. Therefore, they cannot evaluate procurement requirements according to the actual operation conditions of various types of asset equipment, and for the equipment in the retirement stage, the handling is relatively simple, resulting in a high equipment cost. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a digital power plant equipment full-life cycle management method, which has the advantages of reducing downtime losses caused by sudden failures through preventive maintenance and predictive analysis, effectively reducing maintenance costs, optimizing the equipment management process, reducing human errors, and improving management efficiency, and solves the problems of human operation errors, downtime losses caused by sudden failures, high maintenance costs and equipment costs, and poor management efficiency.

[0005] To achieve the above purpose of reducing downtime losses caused by sudden failures through preventive maintenance and predictive analysis, effectively reducing maintenance costs, optimizing the equipment management process, reducing human errors, and improving management efficiency, the present invention provides the following technical solution: A digital power plant equipment full-life cycle management method includes the following steps: Step S1: Digitalization of equipment data. In the design and construction stages, use a digital platform to integrate power plant equipment ledgers, design drawings, and construction record data to form a unified digital asset; Step S2: Intelligent inspection and operation and maintenance. Through the equipment full life cycle management system, realize real-time monitoring, intelligent inspection and predictive maintenance of equipment status; Step S3: Data collection and integration. Use Internet of Things technology to collect equipment operation data, and combine with the associated data generated by the large language model to form a comprehensive data set; Step S4: Causal analysis and indicator management. Through the large language model, conduct causal analysis on the data, identify key indicators, and incorporate them into the equipment full life cycle management system; Step S5: Intelligent decision-making and optimization. Based on the recommendation generation function of the large language model, provide optimization suggestions for equipment operation and maintenance to improve management efficiency.

[0006] Preferably, the specific steps of S1 further include, in the design stage, adopting a digital collaborative design platform to complete the whole-process digital modeling of power plant equipment from preliminary design to as-built drawing design, and conducting coding management on the equipment through the power plant identification system to ensure data intercommunication and integration at different stages.

[0007] Preferably, the digital platform in step S1 will integrate data in stages such as equipment design, construction, and commissioning to form a unified digital asset, providing support for subsequent equipment operation and maintenance.

[0008] Preferably, the equipment full life cycle management system in step S2 includes equipment ledger, intelligent inspection, defect management, and maintenance management modules. Combined with the paperless management process, operators can record the equipment operation status in real time on the mobile terminal to realize the collection and analysis of equipment data at any time.

[0009] Preferably, step S2 further includes using big data and machine learning technologies to conduct real-time monitoring and analysis of equipment operation data, predicting equipment failures in advance, and optimizing maintenance strategies; through digital twin technology, constructing a virtual model of the equipment to realize real-time simulation and optimized operation of the equipment status.

[0010] Preferably, step S3 specifically includes installing a variety of sensors at key parts of power plant equipment, real-time monitoring the operation status of the equipment through these sensors, converting physical quantities into digital signals, and then using the Internet of Things platform or edge computing devices through the Internet of Things technology to regularly read sensor data, using the large language model to conduct semantic association on the existing data, and mining potential associated data, using the large language model to conduct semantic association on the existing data, and mining potential associated data.

[0011] Preferably, step S4 specifically includes using the semantic understanding and reasoning capabilities of the large language model to identify the causal relationships between variables, constructing the identified causal relationships into a causal graph for visualizing the dependencies between device operating parameters, generating counterfactual data through the large language model to quantify the causal effect, and verifying the accuracy of the causal graph using standardized causal graph evaluation metrics.

[0012] Preferably, the optimization measures in step S5 include, during the decommissioning stage of power plant equipment, evaluating the remaining benefits of the power plant equipment, formulating a reasonable decommissioning plan, achieving scientific management during the decommissioning stage of power plant equipment, and ensuring the safety, compliance, and maximization of resource utilization during the decommissioning process.

[0013] Preferably, the evaluation of the remaining benefits of the equipment includes a technical evaluation of inspecting the technical performance, reliability, and compliance with current technical standards of the power plant equipment; an economic evaluation of analyzing the maintenance costs, operating efficiency, and potential economic value of the power plant equipment; and an environmental evaluation of assessing the impact of the decommissioning of the power plant equipment on the environment, especially for power plant equipment that may contain harmful substances.

[0014] Preferably, the decommissioning plan specifically includes clarifying the final state of the power plant equipment after decommissioning, selecting a suitable decommissioning strategy, considering technical feasibility, cost - benefit, and environmental impact, formulating a decommissioning schedule, and clarifying the tasks and responsible persons at each stage.

[0015] Compared with the prior art, the present invention provides a digital full - life - cycle management method for power plant equipment, having the following beneficial effects: 1. This digital full - life - cycle management method for power plant equipment can cover the whole process of power plant equipment from planning, procurement, installation, operation, maintenance to decommissioning, break the information silos at each stage, realize the integration and sharing of data, reduce the downtime losses caused by sudden failures through preventive maintenance and predictive analysis, effectively reduce the maintenance cost, optimize the equipment management process, reduce human errors, and improve the management efficiency.

[0016] 2. This digital full - life - cycle management method for power plant equipment, through using the large language model for causal analysis, identifies the causal relationships between equipment operating parameters, accurately extracts key indicators, improves the scientificity and accuracy of equipment management. Based on big data analysis and machine learning technologies, it explores the operating rules of equipment, optimizes the maintenance strategy, reduces the failure rate. During the decommissioning stage of equipment, through digital evaluation of the remaining benefits of the equipment, formulates a reasonable decommissioning plan, realizes the maximization of resource utilization, discovers potential problems of the equipment in advance, reduces sudden failures, improves equipment reliability, and formulates a scientific maintenance plan based on the actual operating state and data analysis of the equipment to avoid over - maintenance or under - maintenance. Description of the Drawings

[0017] Figure 1 Schematic diagram of the process of a full - life - cycle management method for digital power plant equipment proposed by the present invention. Specific implementation manners

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0019] Please refer to Figure 1 , a full - life - cycle management method for digital power plant equipment, including the following steps: Step S1: Digitalization of equipment data. In the design and construction stages, a digital platform is used to integrate the equipment ledger, design drawings, and construction record data of the power plant to form a unified digital asset. Step S1 specifically further includes, in the design stage, adopting a digital collaborative design platform to complete the whole - process digital modeling of power plant equipment from preliminary design to as - built drawing design, and encoding and managing the equipment through the power plant identification system to ensure data intercommunication and integration in different stages. The digital platform will integrate the data in stages such as equipment design, construction, and commissioning to form a unified digital asset, providing support for subsequent equipment operation and maintenance; Step S2: Intelligent inspection and operation and maintenance. Through the full - life - cycle management system of equipment, real - time monitoring, intelligent inspection, and predictive maintenance of equipment status are realized. The full - life - cycle management system of equipment includes modules such as equipment ledger, intelligent inspection, defect management, and maintenance management. Combined with the paperless management process, operators can record the equipment operation status in real - time on the mobile terminal, realizing the collection and analysis of equipment data at any time. Step S2 also includes using big data and machine learning technologies to monitor and analyze the equipment operation data in real - time, predicting equipment failures in advance, and optimizing maintenance strategies; through digital twin technology, constructing a virtual model of the equipment to realize real - time simulation and optimized operation of the equipment status; Step S3: Data collection and integration. Use Internet of Things technology to collect device operation data and combine it with the associated data generated by large language models to form a comprehensive data set. Specifically, install various sensors at key parts of power plant equipment, such as temperature sensors, pressure sensors, vibration sensors, current / voltage sensors, etc. Through these sensors, monitor the operation status of the equipment in real time. The operation data of the equipment includes fault codes, alarm information, maintenance records, etc., as well as the wear degree and aging condition of the equipment. Convert physical quantities into digital signals. Cameras can also be deployed in key equipment areas to collect the appearance and operation status of the equipment. Computer vision technology can be combined to analyze the collected images and videos to identify abnormal states of the equipment. Then, through Internet of Things technology, use the Internet of Things platform or edge computing devices to regularly read sensor data. Specifically, for example, using Python and related libraries (such as Adafruit_DHT) can achieve data collection of temperature and humidity sensors. Use large language models to perform semantic association on the existing data and mine potential associated data. Use large language models to perform semantic association on the existing data and mine potential associated data. Specifically, for example, the model can analyze equipment fault data and associate other factors that may cause faults, and combine historical data and industry standards to generate associated data related to equipment operation. Preprocess the data before transmission, such as filtering invalid data, data format conversion, etc., to reduce transmission pressure; Step S4: Causal analysis and indicator management. Use a large language model to perform causal analysis on the data, identify key indicators, and incorporate them into the equipment full-life cycle management system. Step S4 specifically includes using the semantic understanding and reasoning capabilities of the large language model to identify causal relationships between variables, constructing the identified causal relationships into a causal diagram for visualizing the dependencies between equipment operation parameters, generating counterfactual data through the large language model to quantify the causal effect, using standardized causal diagram evaluation metrics to verify the accuracy of the causal diagram, specifically determining equipment operation parameters (cause variables) and equipment status (effect variables), such as temperature and equipment failure rate, and using the semantic understanding and reasoning capabilities of the large language model to identify causal relationships between variables. For example, by analyzing historical data and real-time data, the model can discover the causal relationship between "equipment overheating" (cause) and "increased equipment failure rate" (effect), construct the identified causal relationship into a causal diagram for visualizing the dependencies between equipment operation parameters, generate counterfactual data through the large language model to quantify the causal effect. For example, the model can predict the reduction in the failure rate when the equipment temperature is reduced, use standardized causal diagram evaluation metrics (such as the normalized Hamming distance) to verify the accuracy of the causal diagram, extract key indicators from the causal analysis, and these indicators have a significant impact on the equipment status. For example, "equipment operating temperature" and "vibration frequency" may be key indicators affecting the equipment failure rate, incorporate these key indicators into the equipment full-life cycle management system for real-time monitoring, predictive maintenance, and optimizing operation strategies, and based on the results of causal analysis, adjust equipment operation parameters to optimize the operation efficiency and reliability of the equipment; Step S5: Intelligent decision-making and optimization. Based on the recommendation generation function of the large language model, provide optimization suggestions for equipment operation and maintenance to improve management efficiency. The optimization measures in Step S5 include, during the power plant equipment retirement stage, evaluating the remaining benefits of the power plant equipment, formulating a reasonable retirement plan to achieve scientific management during the power plant equipment retirement stage, ensuring the safety, compliance, and maximization of resource utilization during the retirement process. Evaluating the remaining benefits of the equipment includes a technical assessment of checking the technical performance, reliability, and compliance with current technical standards of the power plant equipment; an economic assessment of analyzing the maintenance costs, operation efficiency, and potential economic value of the power plant equipment; an environmental assessment of evaluating the impact of power plant equipment retirement on the environment, especially for power plant equipment that may contain harmful substances. The retirement plan specifically includes clarifying the final state of the power plant equipment after retirement, selecting a suitable retirement strategy considering technical feasibility, cost-effectiveness, and environmental impact, formulating a retirement schedule, clarifying the tasks and responsible persons at each stage, and the handling method of retired equipment depends on its type and characteristics. For equipment that still has use value, it can be considered for repair, renovation, or reuse. For equipment that can no longer be reused, it is recycled, especially for equipment containing harmful substances, and the waste generated from retired equipment is properly handled to meet environmental protection requirements.

[0020] All the electrical components mentioned in this article are electrically connected to the external main controller and the 220V / 380V mains power supply, and the main controller can be a conventional known device such as a computer for control purposes.

[0021] In summary, for this digital power plant equipment full-life cycle management method, first digitize the data of the power plant equipment at the time of factory shipment. During actual use, conduct real-time monitoring, intelligent inspection, and predictive maintenance, collect data, then integrate the data, perform causal analysis and indicator management. Then, for retired power plant equipment, evaluate the remaining benefits of the power plant equipment, formulate a reasonable retirement plan, achieve scientific management in the retirement stage of power plant equipment, ensure the safety, compliance, and maximized utilization of resources during the retirement process, cover the entire process of power plant equipment from planning, procurement, installation, operation, maintenance to retirement, break the information silos at each stage, realize the integration and sharing of data, reduce the downtime losses caused by sudden failures through preventive maintenance and predictive analysis, effectively reduce the maintenance cost, optimize the equipment management process, reduce human errors, and improve the management efficiency.

[0022] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or sequence between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one" does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0023] In this application, the orientation or positional relationship indicated by terms such as "upper", "lower", "inner", "middle", "outer", "front", "rear", etc. is based on the orientation or positional relationship shown in the drawings. These terms are mainly used to better describe this application and its embodiments, and are not used to limit that the indicated device, element or component must have a specific orientation, or be constructed and operated in a specific orientation.

[0024] Moreover, in addition to being able to represent an orientation or positional relationship, some of the above terms may also be used to represent other meanings. For example, the term "upper" may also be used to represent a certain attachment relationship or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to specific circumstances.

[0025] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for the full life cycle management of digital power plant equipment, characterized in that: It includes the following steps: Step S1: Digitalization of equipment data. In the design and construction stages, use a digital platform to integrate the power plant equipment ledger, design drawings, and construction record data to form a unified digital asset. Step S2: Intelligent inspection and operation and maintenance. Through the equipment full-life cycle management system, realize real-time monitoring, intelligent inspection, and predictive maintenance of equipment status. Step S3: Data collection and integration. Use Internet of Things technology to collect equipment operation data, and combine it with the associated data generated by the large language model to form a comprehensive data set. Step S4: Causal analysis and index management. Through the large language model, conduct causal analysis on the data, identify key indicators, and incorporate them into the equipment full-life cycle management system. Step S5: Intelligent decision-making and optimization. Based on the recommendation generation function of the large language model, provide optimization suggestions for equipment operation and maintenance to improve management efficiency.

2. A full-life cycle management method for digital power plant equipment according to claim 1, characterized in that: Specifically, Step S1 further includes, in the design stage, using a digital collaborative design platform to complete the whole-process digital modeling of power plant equipment from preliminary design to as-built drawing design, and conducting coding management on the equipment through the power plant identification system to ensure data interconnection and integration in different stages.

3. A full-life cycle management method for digital power plant equipment according to claim 1, characterized in that: The digital platform in Step S1 will integrate the data in the equipment design, construction, commissioning and other stages to form a unified digital asset, providing support for subsequent equipment operation and maintenance.

4. A method for full life cycle management of digital power plant equipment according to claim 1, characterized in that: The equipment full-life cycle management system in Step S2 includes equipment ledger, intelligent inspection, defect management, and maintenance management modules. Combined with the paperless management process, operators can record the equipment operation status in real time on the mobile terminal to realize the timely collection and analysis of equipment data.

5. A method for full life cycle management of digital power plant equipment according to claim 1, characterized in that: Step S2 also includes using big data and machine learning technologies to conduct real-time monitoring and analysis of equipment operation data, predict equipment failures in advance, and optimize maintenance strategies; through digital twin technology, construct a virtual model of the equipment to realize real-time simulation and optimized operation of the equipment status.

6. The full - life - cycle management method for digital power plant equipment according to claim 1, characterized in that: Specifically, Step S3 includes installing a variety of sensors at key parts of power plant equipment, using these sensors to monitor the operation status of the equipment in real time, converting physical quantities into digital signals, and then using the Internet of Things platform or edge computing devices through the Internet of Things technology to regularly read the sensor data, using the large language model to conduct semantic association on the existing data, and mining potential associated data.

7. A method for full life cycle management of digital power plant equipment according to claim 1, characterized in that: Specifically, Step S4 includes using the semantic understanding and reasoning ability of the large language model to identify the causal relationship between variables, constructing the identified causal relationship into a causal graph for visualizing the dependency relationship between equipment operation parameters, generating counterfactual data through the large language model to quantify the causal effect, and using standardized causal graph evaluation indicators to verify the accuracy of the causal graph.

8. A method for full - life - cycle management of digital power plant equipment according to claim 1, characterized in that: The optimization measures in Step S5 include, in the equipment retirement stage of the power plant, evaluating the remaining benefits of the power plant equipment, formulating a reasonable retirement plan, realizing scientific management in the equipment retirement stage of the power plant, and ensuring the safety, compliance, and maximized utilization of resources in the retirement process.

9. A method for full life cycle management of digital power plant equipment according to claim 8, characterized in that: The remaining benefits of the said evaluation equipment include technical evaluations for inspecting the technical performance, reliability, and compliance with current technical standards of power plant equipment; economic evaluations for analyzing the maintenance costs, operating efficiency, and potential economic value of power plant equipment; and environmental evaluations for assessing the environmental impact of the decommissioning of power plant equipment, especially for power plant equipment that may contain hazardous substances.

10. A method for full - life - cycle management of digital power plant equipment according to claim 8, characterized in that: The said decommissioning plan specifically includes clarifying the final state of the power plant equipment after decommissioning, selecting appropriate decommissioning strategies, considering technical feasibility, cost-effectiveness, and environmental impact, formulating a decommissioning schedule, and clarifying the tasks and responsible persons for each stage.

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