CN120145860A - A method for constructing a digital twin power plant - Google Patents
A method for constructing a digital twin power plant Download PDFInfo
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- CN120145860A CN120145860A CN202510287992.9A CN202510287992A CN120145860A CN 120145860 A CN120145860 A CN 120145860A CN 202510287992 A CN202510287992 A CN 202510287992A CN 120145860 A CN120145860 A CN 120145860A
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- G05B19/00—Programme-control systems
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- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
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Abstract
本发明公开了一种数字孪生电厂构建方法，涉及数字孪生技术领域。其中，该方法包括：采用多种传感器实时对电厂设备运行状态进行数据采集，通过云计算平台对多源数据进行融合，形成统一的数据格式；基于融合后的全息数据，利用机器学习和人工智能技术构建数字孪生模型；通过实时数据流处理技术，使得数字孪生模型与实际电厂设备状态同步；采用自适应优化算法，根据实时数据反馈调整数字孪生模型的参数。通过云计算平台对多源数据进行融合，仅降低了数据处理的复杂度，还提高了数据的可用性和一致性，为数字孪生模型的构建和运行提供了强有力的支持。通过实时数据流处理技术，确保了数字孪生模型与实际电厂设备状态的高度同步。
The present invention discloses a method for constructing a digital twin power plant, and relates to the field of digital twin technology. The method includes: using a variety of sensors to collect data on the operating status of power plant equipment in real time, fusing multi-source data through a cloud computing platform to form a unified data format; based on the fused holographic data, using machine learning and artificial intelligence technology to construct a digital twin model; using real-time data stream processing technology to synchronize the digital twin model with the actual power plant equipment status; using an adaptive optimization algorithm to adjust the parameters of the digital twin model according to real-time data feedback. Fusion of multi-source data through a cloud computing platform not only reduces the complexity of data processing, but also improves the availability and consistency of data, providing strong support for the construction and operation of digital twin models. Through real-time data stream processing technology, a high degree of synchronization between the digital twin model and the actual power plant equipment status is ensured.
Description
Technical Field
The invention relates to the technical field of digital twin, in particular to a method for constructing a digital twin power plant.
Background
As a complex industrial system, the power plant has numerous devices and complex operation parameters, and the traditional management and maintenance modes are difficult to meet the requirements of high efficiency and safety. As an emerging solution, the digital twin technology can realize the management and optimization of the whole life cycle of the entity object by creating a digital twin model of the entity object. However, the existing digital twin technology still faces many challenges when applied to a power plant, such as incomplete data acquisition, insufficient model precision, poor real-time performance and the like.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a digital twin power plant construction method.
The invention provides a digital twin power plant construction method, which comprises the following steps:
The method comprises the steps of adopting a plurality of sensors to collect data of the running state of power plant equipment in real time, and fusing multi-source data through a cloud computing platform to form a unified data format;
Based on the fused holographic data, constructing a digital twin model by utilizing machine learning and artificial intelligence technology;
synchronizing the digital twin model with the actual power plant equipment state through a real-time data stream processing technology;
And adopting a self-adaptive optimization algorithm to adjust parameters of the digital twin model according to real-time data feedback.
Optionally, a time sequence database is used for compressing and storing the dither data.
Optionally, the plurality of sensors includes at least two of temperature, pressure, flow and vibration sensors.
Optionally, edge compute nodes are deployed at the device to enable data cleansing and feature extraction.
Optionally, the abnormal acquisition node is automatically identified by deploying a data quality monitoring module.
Optionally, version tracing of the digital twin model is achieved by constructing a digital thread.
Optionally, the running state of the equipment is analyzed and predicted through an intelligent algorithm, and decision suggestions of optimizing running, preventive maintenance and upgrading transformation are provided for a power plant manager.
Optionally, the full life cycle of the power plant is managed by a digital twin model.
Optionally, a security monitoring system is established to monitor and pre-warn for potential security threats in real time.
Optionally, a device health management system is established, and preventive maintenance and fault prediction of the device are realized by combining a digital twin model.
Compared with the prior art, the technical scheme of the invention fuses the multi-source data through the cloud computing platform, so that the complexity of data processing is reduced, the usability and consistency of the data are improved, and powerful support is provided for the construction and operation of the digital twin model. By means of a real-time data stream processing technology, high synchronization of the digital twin model and the actual power plant equipment state is ensured. The real-time performance ensures that the model can accurately reflect the latest state of the equipment, and provides a reliable basis for real-time management and decision making of the power plant. The self-adaptive optimization algorithm can automatically adjust parameters of the digital twin model according to real-time data feedback, and the prediction accuracy and adaptability of the model are improved.
Drawings
In order to more clearly explain the technical solutions in the embodiments of the present application, the drawings that are used in the embodiments of the present application will be described below.
FIG. 1 is a flow chart of a digital twin power plant construction method provided by the invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The terms "first", "second", and the like in the embodiments of the present application are merely for distinguishing related technical features, and do not indicate a sequence. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the application herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the present application, the terms "upper", "lower", "inner", "middle", "outer", "front", "rear", and the like indicate an azimuth or a positional relationship based on that shown in the drawings. These terms are only used to better describe the present application and its embodiments and are not intended to limit the scope of the indicated devices, elements or components to the particular orientations or to configure and operate in the particular orientations.
Also, some of the terms described above may be used to indicate other meanings in addition to orientation or positional relationships, for example, the term "upper" may also be used to indicate some sort of attachment or connection in some cases. The specific meaning of these terms in the present application will be understood by those of ordinary skill in the art according to the specific circumstances.
According to the method for constructing the digital twin power plant, the cloud computing platform is used for fusing the multi-source data, so that the complexity of data processing is reduced, the usability and consistency of the data are improved, and powerful support is provided for construction and operation of the digital twin model. By means of a real-time data stream processing technology, high synchronization of the digital twin model and the actual power plant equipment state is ensured. The real-time performance ensures that the model can accurately reflect the latest state of the equipment, and provides a reliable basis for real-time management and decision making of the power plant. The self-adaptive optimization algorithm can automatically adjust parameters of the digital twin model according to real-time data feedback, and the prediction accuracy and adaptability of the model are improved.
Examples
The embodiment of the invention provides a method for constructing a digital twin power plant, as shown in fig. 1, which comprises the following steps:
S110, acquiring data of the running state of power plant equipment in real time by adopting a plurality of sensors, and fusing multi-source data through a cloud computing platform to form a unified data format;
s120, constructing a digital twin model by utilizing machine learning and artificial intelligence technology based on the fused holographic data;
S130, synchronizing the digital twin model with the actual power plant equipment state through a real-time data stream processing technology;
and S140, adopting an adaptive optimization algorithm, and adjusting parameters of the digital twin model according to real-time data feedback.
Therefore, the operation state data of the power plant equipment are collected in real time through various sensors, and the cloud computing platform is utilized for multi-source data fusion, so that a unified data format is formed, and the comprehensive and real-time monitoring of the power plant equipment is realized. The data fusion improves the consistency and accuracy of the data, and provides a high-quality data basis for the subsequent digital twin model construction. Based on the fused holographic data, a digital twin model is constructed by utilizing machine learning and artificial intelligence technology, so that intelligent modeling and simulation of power plant equipment are realized. The digital twin model can accurately reflect the running state of actual power plant equipment, and provides a powerful simulation and analysis tool for the operation and management of the power plant. The digital twin model is synchronized with the actual power plant equipment state through a real-time data stream processing technology, and the consistency of the model and the actual equipment is ensured. Real-time synchronization is helpful for timely finding out equipment abnormality and improving fault diagnosis and processing efficiency. And the adaptive optimization algorithm is adopted, and parameters of the digital twin model are adjusted according to real-time data feedback, so that the adaptability and accuracy of the model are improved. The self-adaptive optimization is beneficial to maintaining high-precision simulation effect of the model under different operation conditions, and powerful support is provided for the optimized operation of the power plant. The digital twin model can be used for predictive maintenance, potential problems are found in advance by analyzing model data, unexpected downtime is reduced, and the operation and maintenance efficiency of the power plant is improved. The digital twin model is used for simulation and analysis, so that the operation strategy of the power plant can be optimized, and the energy consumption and the operation cost can be reduced. The digital twin model provides rich data and analysis results, provides powerful decision support for power plant management personnel, and is beneficial to making a more scientific and reasonable operation plan. Real-time monitoring and intelligent analysis are beneficial to timely finding potential safety hazards, measures are taken to prevent accidents, and safety performance of a power plant is improved.
More specifically, the time series database is used for compressing and storing the dither data. It can be appreciated that the time series database is used for compressing and storing the dither data, so that the requirement of the data storage space is remarkably reduced. The compression technique ensures data integrity and improves the efficiency of data storage and transmission. The time series database optimizes the data retrieval speed, making it possible to quickly locate and analyze dither data for a specific period of time in a large amount of historical data. The rapid search is helpful to respond to the abnormality of the equipment in time and shorten the fault diagnosis time. By efficient data compression, the cost of long-term storage of dither data is reduced. The investment and maintenance cost of the storage equipment are reduced, and the overall economy is improved. The time series database supports complex data analysis operations such as trend analysis, anomaly detection, and the like. The performance and the accuracy of the digital twin model in processing and analyzing the high-frequency vibration data are improved. In combination with real-time data stream processing techniques, the time series database is capable of receiving, compressing and storing dither data in real-time. The real-time performance of the synchronization of the digital twin model and the actual power plant equipment state is enhanced, and the response speed of the monitoring system is improved. Efficient data compression and storage provides a large amount of high quality training data for machine learning and artificial intelligence algorithms. The training process of the digital twin model is optimized, and the prediction precision and generalization capability of the model are improved. The design of the time sequence database is specially aimed at the storage and inquiry of the time sequence data, so that the stability and reliability of the system are improved. The risk of system failure or data loss caused by improper data management is reduced. The efficient compression storage enables long-term accumulation of high-frequency vibration data to be feasible, and data support is provided for long-term operation analysis and optimization of a power plant. The research on the long-term operation trend of the equipment is supported, and the potential long-period problem is found.
By adopting a time sequence database to compress and store high-frequency vibration data, the method for constructing the digital twin power plant realizes remarkable improvement in the aspects of data management, analysis efficiency, cost control and the like, and further enhances the application value and effect of the digital twin technology in the operation and maintenance management of the power plant.
More specifically, the plurality of sensors may include at least two of temperature, pressure, flow and vibration sensors. Therefore, the multidimensional and comprehensive operation state data acquisition of the power plant equipment is realized by integrating at least two of temperature, pressure, flow and vibration sensors. Multidimensional data provides more information and helps to more accurately analyze and determine device status. The reasons and positions of equipment faults can be diagnosed more accurately by combining the data of various sensors. For example, by analyzing the temperature and vibration data simultaneously, problems such as bearing failure or thermal imbalance can be more effectively identified. The data fusion of various sensors provides more comprehensive input characteristics for machine learning and artificial intelligence algorithms, and improves the accuracy of predictive maintenance. Potential problems can be found earlier, maintenance can be carried out in advance, and unexpected shutdown is avoided. By analyzing the data of the temperature, pressure, flow and other sensors, the operation parameters of the equipment can be adjusted in real time, and the operation efficiency is optimized. For example, the pump operating speed is adjusted based on the flow and pressure data to maximize energy efficiency. The real-time monitoring of various sensors is helpful for timely finding potential safety hazards, such as overheat, overpressure and the like, and measures are immediately taken to improve the safety of the power plant. The data provided by different sensors have complementarity, can mutually verify, and reduce the situations of false alarm and missing report. For example, the abnormal vibration detected by the vibration sensor can be further confirmed by the data of the temperature sensor whether it is caused by overheat. Based on the data of various sensors, the comprehensive performance of the power plant equipment can be evaluated. The comprehensive performance evaluation helps to make more reasonable maintenance and upgrade plans. By means of data analysis and predictive maintenance of various sensors, unnecessary maintenance operations can be reduced, and maintenance cost can be reduced. The situation of excessive maintenance or insufficient maintenance is avoided, and the maintenance efficiency is improved. The data fusion of various sensors enables the digital twin model to be more vivid and reflect the running state of actual equipment more accurately. The application value of the digital twin model in simulation, analysis and optimization is improved.
More specifically, edge computing nodes are deployed at the device to enable data cleaning and feature extraction. And the edge computing nodes are arranged at the side of the equipment, so that the collected data can be cleaned and preprocessed in real time, and noise, abnormal values and redundant information are removed. The real-time data cleaning ensures the data quality of subsequent analysis and improves the effectiveness and accuracy of the data. The edge computing node performs cleaning and feature extraction on the equipment side, so that the data volume required to be transmitted to the cloud is reduced. Reducing the data transmission burden is beneficial to relieving the network pressure and improving the data transmission efficiency. The edge computing nodes can quickly extract key features and provide directly available input for the digital twin model. The rapid feature extraction accelerates the data processing flow and shortens the response time from data acquisition to model application. The data is processed on the equipment side, so that the exposure risk of the data in the transmission process is reduced. The data security is enhanced, and the sensitive information and intellectual property rights of the power plant are protected. The edge computing nodes provide distributed computing power and share the computing pressure of the cloud platform. Distributed computing increases the processing power of the overall system, supporting access to more devices and more complex data analysis. The edge computing nodes can detect the device state in real time and respond to potential faults quickly. The fault response speed is improved, and the influence of faults on the operation of the power plant is reduced. By performing cleaning and feature extraction on the device side, the utilization of computing resources is optimized. Unnecessary resource waste is avoided, and resource utilization efficiency is improved. The edge computing node may continue data cleaning and feature extraction in the event of network instability or disconnection. Supporting offline operation enhances the robustness and reliability of the system. The data preprocessed by the equipment side is directly transmitted to the cloud, so that the processing flow of the cloud is simplified. The process is simplified, and the cloud processing efficiency and accuracy are improved. The real-time data cleaning and feature extraction functions provided by the edge computing nodes enhance the real-time performance of the digital twin model. The digital twin model can be more tightly synchronized with the actual equipment state, and the simulation and prediction accuracy is improved.
More specifically, the abnormal acquisition node is automatically identified by deploying a data quality monitoring module. The deployment data quality monitoring module can automatically identify the abnormal acquisition node without manual intervention. And detecting abnormality in real time, and ensuring the accuracy and reliability of data. By automatically identifying and eliminating abnormal data, the overall data quality is significantly improved. Providing a more accurate and more reliable data base for the digital twin model. Automated anomaly detection reduces the amount of manual inspection and data cleaning effort. The labor cost is reduced, and the data processing efficiency is improved. And identifying and isolating abnormal acquisition nodes in time, and preventing the normal operation of the system from being influenced by error data. The robustness and stability of the system are enhanced. The data quality monitoring module can rapidly locate abnormal sources, and is convenient for timely maintaining or replacing fault equipment. The fault recovery time is shortened, and the influence on the operation of the power plant is reduced. After the abnormal data is removed, the data fusion process is smoother, and the result is more accurate. And the data fusion efficiency is improved, and the data processing flow is optimized. The digital twin model constructed based on the high-quality data has higher accuracy. And the effects of model simulation, prediction and optimization decision are improved. High quality data and accurate model results enhance confidence in the decision. Is beneficial to formulating more scientific and reasonable operation strategies. Automatically identifying abnormal collection nodes helps to prevent potential data errors and system risks. Measures are taken in advance, so that accidents are avoided. The data quality monitoring module is suitable for deployment of a large-scale sensor network. And more devices are supported to be accessed, and the system scale is expanded.
More specifically, the version tracing of the digital twin model can be realized by constructing a digital thread. It can be appreciated that the digital thread records the complete history of the digital twin model from creation to each modification, enabling accurate version control of the model. The method is convenient for users to check, compare and trace back any historical version, and ensures the continuity and consistency of the model. All changes of the model, including information such as change time, change content and change personnel, can be traced back clearly through the digital thread. The traceability of the model is improved, and auditing and compliance checking are facilitated. The version tracing function enables the model iterative process to be more transparent and orderly.
Team cooperation can be facilitated, multiple persons work in different versions at the same time, and finally optimization results are combined. When a problem occurs in the model, the problem version can be quickly located through version tracing. And the system is quickly restored to a stable version, and the influence of faults on the operation of the power plant is reduced. By continuously tracking and recording model changes, the quality and reliability of the model is ensured. The accumulation of problems due to untracked changes is avoided. A complete view of the evolution of the model is provided, providing powerful support for the management layer decision making. Based on historical data and analysis, a more scientific and reasonable operation strategy is formulated. The digital thread records the overall process of model construction and optimization, forming valuable knowledge assets. The method is beneficial to knowledge sharing and inheritance, and improves the overall skill level of the team. By version tracing, confusion and repeated work caused by model change are reduced. The maintenance cost of the model is reduced, and the working efficiency is improved. The clear version history allows the excellent model to be easily reused and generalized. The reusability of the model is improved, and development resources are saved. Digital threads provide the basis for continuous improvement models. Model performance and accuracy are continuously optimized by analyzing historical versions.
More specifically, the running state of the equipment is analyzed and predicted through an intelligent algorithm, and decision suggestions for optimizing running, preventive maintenance and upgrading transformation are provided for a power plant manager. Based on the analysis result of the intelligent algorithm, the operation parameters of the equipment are adjusted in real time, so that the equipment is ensured to operate in an optimal state all the time, and the energy efficiency and the power generation capacity are improved. Through intelligent optimization, unnecessary energy waste is reduced, and energy conservation and emission reduction are realized. The intelligent algorithm can predict potential faults and abrasion conditions of equipment, early warning is sent out in advance, and shutdown loss caused by sudden faults is avoided. Through regularly carrying out preventive maintenance, effectively extension equipment life reduces change frequency and cost. Based on the intelligent algorithm, the device operation data is deeply analyzed, and scientific and data-driven decision basis is provided for upgrading and reconstruction. And through a prediction model, the return on investment of different upgrading reconstruction schemes is evaluated, so that a manager is helped to select the most economical scheme. The intelligent algorithm replaces the traditional manual experience judgment, reduces human errors and improves the accuracy and reliability of decision making. And multiple factors are comprehensively considered for decision analysis, so that the comprehensiveness and rationality of the decision are ensured. The intelligent algorithm can identify potential risks in equipment operation, measures are taken in time, and safety management of the power plant is enhanced. Through prediction and preventive maintenance, the probability of accident occurrence is reduced, and the safety of personnel and equipment is ensured. The intelligent algorithm realizes automatic monitoring and adjustment of equipment operation, reduces manual intervention, and improves operation efficiency. And optimizing resource allocation according to the running state of the equipment and the prediction result, and improving the resource utilization efficiency. Preventive maintenance reduces the need and cost of sudden maintenance. Based on the predicted spare part demands, inventory management is optimized, and spare part storage cost is reduced. Advanced intelligent algorithm technology is adopted, so that the technical level and market competitiveness of the power plant are improved. Decision support based on intelligent analysis enables the power plant to more flexibly cope with market changes and demands.
More specifically, the full life cycle of the power plant equipment can be managed through a digital twin model.
Thus, in the device design stage, the digital twin model can be used as a virtual prototype to help a designer perform all-round simulation and analysis on the device in a virtual environment. This not only allows for early discovery of potential design issues such as insufficient structural strength, excessive thermal deformation, etc., but also allows for predicting and optimizing the performance of the device. Through digital twin technology, the designer can simulate the running condition of equipment under different working conditions, including cutting force, vibration, temperature distribution and the like, so that the reliability and the effectiveness of the design scheme are ensured. In the equipment manufacturing and installation stage, the digital twin model can be used as production guidance to help manufacturers to realize the intellectualization and visualization of the production process. By constructing a digital twin model of production equipment and a production line, operation data and production data of the equipment are collected in real time, and enterprises can comprehensively monitor and optimize the production process. This not only improves the production efficiency but also ensures consistency of the product quality. Meanwhile, the digital twin model can also be used for virtual debugging of equipment, so that the debugging cost and period are reduced, and the safety and accuracy of debugging are improved. In the operation stage of the equipment, the digital twin model can be used as a real-time monitoring and early warning system to help the power plant to realize preventive maintenance of the equipment. By monitoring the running state and performance data of the equipment in real time, the digital twin model can predict when the equipment is likely to fail and give early warning in advance. This not only reduces unexpected downtime and maintenance costs, but also improves the reliability and stability of the device. In addition, the digital twin model can also be used for remote monitoring and supporting of equipment, and an engineer can view the state information of the equipment through the digital twin platform in real time for remote diagnosis and supporting regardless of geographical positions. During the equipment maintenance phase, the digital twin model may provide maintenance planning and optimization recommendations. By recording the maintenance history and performance data of the equipment, the digital twin model can analyze the maintenance requirements and period of the equipment and make corresponding maintenance plans. This can reduce maintenance costs and can also improve the utilization and reliability of the device. Meanwhile, the digital twin model can also be used for fault diagnosis and elimination of equipment, and fault points can be rapidly positioned and solutions can be provided by simulating and analyzing the running state of the equipment. In the equipment retirement stage, the digital twin model can be used as an evaluation tool to help a power plant to make a scientific scrapping plan. By recording full life cycle data of the equipment, including frequency of use, maintenance history, etc., the digital twin model can evaluate the remaining life and performance status of the equipment, providing reasonable rejection advice for the power plant. The method can avoid resource waste caused by premature scrapping of equipment still having use value, and can also prevent potential safety hazard and operation cost increase caused by the fact that evening paper is used for waste equipment which cannot be used normally.
More specifically, a safety monitoring system is established to monitor and pre-warn potential safety threats in real time. The safety monitoring system realizes 24/7 uninterrupted real-time monitoring through sensors deployed at key parts of the power plant, and ensures that any abnormal situation can be captured in time. Once the potential security threat is detected, the system immediately gives an early warning to inform related personnel to quickly take countermeasures to prevent accidents. And analyzing the collected data by utilizing machine learning and artificial intelligence technology, predicting potential safety risk, and realizing early prevention. Through early warning and intervention in advance, the occurrence of power plant accidents is effectively reduced, and the safety of personnel and equipment is ensured. The safety monitoring system provides real-time data support, so that an emergency response team can quickly know the accident situation and formulate effective emergency measures. The system supports cooperative combat of multiple departments, and improves the efficiency and effect of emergency response. The safety monitoring system provides comprehensive and accurate data support and helps managers to make scientific and reasonable decisions. Based on the data analysis result, the safety strategy and measures are optimized, and the overall safety level of the power plant is improved. Through real-time monitoring and early warning, accurate maintenance is realized, excessive maintenance or insufficient maintenance is avoided, and maintenance cost is reduced. And problems are found and treated in time, the service life of equipment is prolonged, and the replacement cost is reduced. The establishment and operation of the safety monitoring system are beneficial to improving the safety consciousness of all staff. The construction of the safety culture of the power plant is promoted, and a good atmosphere that people pay attention to safety and people participate in safety is formed. And the safety monitoring system is established to meet the requirements of related laws and standards, so that the compliance operation of the power plant is ensured. By monitoring and early warning of the system, legal risks and reimbursement responsibilities caused by safety accidents are avoided. The operation condition of the safety monitoring system can be disclosed outwards, the transparency of the power plant is improved, and the public trust is enhanced. By guaranteeing the safety of the power plant, the social responsibility of the enterprise is fulfilled, and the good enterprise image is built.
More specifically, a device health management system is established, and preventive maintenance and fault prediction of the device are realized by combining a digital twin model. The equipment health management system is a comprehensive management platform, integrates various sensors, data analysis algorithms and prediction models, and is used for monitoring and analyzing the running states of power plant equipment in real time. The core objective of the system is to discover potential faults of equipment in advance, and to achieve preventive maintenance, thereby reducing maintenance cost and improving reliability and stability of the equipment. The digital twin model can reflect the running state of power plant equipment in real time, and the running state comprises key parameters such as temperature, pressure, vibration and the like. By collecting and analyzing these real-time data, the system can discover the abnormal state or potential failure of the device in time. Based on the prediction result of the digital twin model, the power plant can make a maintenance plan in advance to perform preventive maintenance on the equipment. The device can not only reduce unexpected downtime, but also prolong the service life of the device and reduce maintenance cost. The digital twin model is capable of modeling the operating state of the device, predicting the type and time of failure that may occur. When the equipment fails, the system can quickly locate the failure point and provide a corresponding maintenance scheme. The system collects data in real time through various sensors installed on the power plant equipment. These data are transmitted over a communication network to a central processing unit for analysis and processing. The central processing unit analyzes the acquired data using advanced algorithms and models. By comparing the digital twinning model to the state of the actual device, the system can predict the health and potential failure of the device. Based on the prediction, the system may automatically generate a maintenance schedule, including maintenance time, maintenance content, required spare parts, and the like. These plans may be updated in real time to ensure compliance with the actual state of the device. When the equipment fails, the system can quickly locate the failure point and provide a corresponding maintenance scheme. Maintenance personnel can conduct fault removal work according to the guidance provided by the system, and maintenance efficiency is improved.
Thus, through preventive maintenance and fault prediction, the system can remarkably reduce the fault rate of equipment and improve the reliability and stability of the equipment. By making maintenance plans in advance and locating fault points quickly, the system can reduce maintenance costs and time costs. The implementation of the equipment health management system can optimize the production flow of the power plant and improve the production efficiency and quality. Through the real-time monitoring and early warning of the equipment, the system can improve the safety management level of the power plant and reduce the occurrence of safety accidents.
Compared with the prior art, the method for constructing the digital twin power plant, provided by the embodiment of the invention, has the advantages that the cloud computing platform is used for fusing the multi-source data, so that the complexity of data processing is reduced, the usability and consistency of the data are improved, and a powerful support is provided for the construction and operation of a digital twin model. By means of a real-time data stream processing technology, high synchronization of the digital twin model and the actual power plant equipment state is ensured. The real-time performance ensures that the model can accurately reflect the latest state of the equipment, and provides a reliable basis for real-time management and decision making of the power plant. The self-adaptive optimization algorithm can automatically adjust parameters of the digital twin model according to real-time data feedback, and the prediction accuracy and adaptability of the model are improved.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read Only Memory (ROM), a random access memory (RandomAccessMemory, RAM), a FLASH memory (FLASH), a hard disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method of the embodiments of the present invention.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
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