Digital twin power plant construction method

Abstract

The invention discloses a digital twinning power plant construction method, and relates to the technical field of digital twinning. The method comprises the following steps: performing data acquisition on the operation 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 uniform data format; based on the fused holographic data, constructing a digital twinborn model by using machine learning and artificial intelligence technologies; through a real-time data stream processing technology, the digital twin model is synchronized with an actual power plant equipment state; and adjusting parameters of the digital twinborn model according to real-time data feedback by adopting a self-adaptive optimization algorithm. The multi-source data is fused through the cloud computing platform, so that the complexity of data processing is reduced, the availability and consistency of the data are improved, and powerful support is provided for construction and operation of the digital twin model. Through a real-time data stream processing technology, high synchronization of the digital twin model and an actual power plant equipment state is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and particularly to a method for constructing a digital twin power plant. Background Art

[0002] As a complex industrial system, a power plant has numerous devices and complex operating parameters. Traditional management and maintenance methods are difficult to meet the requirements of high efficiency and safety. Digital twin technology, as an emerging solution, can achieve the management and optimization of the entire life cycle of an entity object by creating a digital twin model of the entity object. However, when existing digital twin technologies are applied to power plants, many challenges still exist, such as incomplete data collection, insufficient model accuracy, poor real-time performance, and other problems. Summary of the Invention

[0003] In order to solve at least one of the above technical problems, the present invention provides a method for constructing a digital twin power plant.

[0004] The present invention provides a method for constructing a digital twin power plant, including: Using a variety of sensors to collect data on the operating status 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, using machine learning and artificial intelligence technologies to construct a digital twin model; Through real-time data stream processing technology, synchronizing the digital twin model with the actual status of power plant equipment; Using an adaptive optimization algorithm to adjust the parameters of the digital twin model according to real-time data feedback.

[0005] Optionally, a time series database is used for compressed storage of high-frequency vibration data.

[0006] Optionally, the variety of sensors includes at least two of temperature, pressure, flow, and vibration sensors.

[0007] Optionally, edge computing nodes are deployed on the device side to achieve data cleaning and feature extraction.

[0008] Optionally, by deploying a data quality monitoring module, abnormal acquisition nodes are automatically identified.

[0009] Optionally, a digital thread is constructed to achieve version traceability of the digital twin model.

[0010] Optionally, through intelligent algorithms, the operating status of equipment is analyzed and predicted, providing decision-making suggestions for power plant managers for optimized operation, preventive maintenance, and upgrade and transformation.

[0011] Optionally, the entire life cycle of power plant equipment is managed through the digital twin model.

[0012] Optionally, a safety monitoring system is established to monitor and give early warnings of potential safety threats in real time.

[0013] Optionally, an equipment health management system is established to realize preventive maintenance and fault prediction of equipment in combination with the digital twin model.

[0014] Compared with the prior art, the technical solution of the present invention fuses multi-source data through the cloud computing platform, which 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 the digital twin model. Through the real-time data stream processing technology, the high synchronization between the digital twin model and the actual power plant equipment status is ensured. This real-time nature ensures that the model can accurately reflect the latest status of the equipment, providing a reliable basis for the real-time management and decision-making of the power plant. The adaptive optimization algorithm can automatically adjust the parameters of the digital twin model according to the real-time data feedback, improving the prediction accuracy and adaptability of the model. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments of the present application will be described below.

[0016] Figure 1 It is a flowchart of a method for constructing a digital twin power plant provided by the present invention. Detailed Embodiments

[0017] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0018] Terms such as "first" and "second" in the embodiments of the present invention are only used to distinguish related technical features and do not represent a sequence. It should be understood that such data can be interchanged under appropriate circumstances for the embodiments of the present application described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0019] In this application, the orientation or positional relationship indicated by terms such as "upper", "lower", "inner", "middle", "outer", "front", and "rear" 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 devices, elements, or components must have a specific orientation, or be constructed and operated in a specific orientation.

[0020] Moreover, in addition to being used to represent the 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.

[0021] A method for constructing a digital twin power plant provided by the present invention fuses multi-source data through a cloud computing platform, which 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 the digital twin model. Through real-time data stream processing technology, the high synchronization between the digital twin model and the actual power plant equipment status is ensured. This real-time nature ensures that the model can accurately reflect the latest status of the equipment, providing a reliable basis for the real-time management and decision-making of the power plant. The adaptive optimization algorithm can automatically adjust the parameters of the digital twin model according to real-time data feedback, improving the prediction accuracy and adaptability of the model. Embodiment

[0022] A method for constructing a digital twin power plant provided by an embodiment of the present invention, as Figure 1 shown, the method includes: S110. Use a variety of sensors to collect data on the operating status of power plant equipment in real time, and fuse the multi-source data through a cloud computing platform to form a unified data format; S120. Based on the fused holographic data, construct a digital twin model using machine learning and artificial intelligence technologies; S130. Through real-time data stream processing technology, synchronize the digital twin model with the actual power plant equipment status; S140. Use an adaptive optimization algorithm to adjust the parameters of the digital twin model according to real-time data feedback.

[0023] In this way, the operating status data of power plant equipment are collected in real time through multiple sensors, and multi-source data fusion is carried out using a cloud computing platform to form a unified data format, realizing comprehensive and real-time monitoring of power plant equipment. Data fusion improves the consistency and accuracy of data, providing a high-quality data foundation for the subsequent construction of digital twin models. Based on the fused holographic data, digital twin models are constructed using machine learning and artificial intelligence technologies, achieving intelligent modeling and simulation of power plant equipment. The digital twin models can accurately reflect the operating status of actual power plant equipment, providing powerful simulation and analysis tools for the operation and maintenance of power plants. Through real-time data stream processing technology, the digital twin models are synchronized with the actual power plant equipment status, ensuring the consistency between the models and the actual equipment. Real-time synchronization helps to detect equipment anomalies in a timely manner and improve the efficiency of fault diagnosis and handling. An adaptive optimization algorithm is adopted to adjust the parameters of the digital twin models according to real-time data feedback, improving the adaptability and accuracy of the models. Adaptive optimization helps the models maintain high-precision simulation effects under different operating conditions, providing strong support for the optimized operation of power plants. The digital twin models can be used for predictive maintenance, discovering potential problems in advance by analyzing model data, reducing unexpected downtime, and improving the operation and maintenance efficiency of power plants. Through simulation and analysis using digital twin models, the operation strategies of power plants can be optimized, reducing energy consumption and operating costs. The digital twin models provide rich data and analysis results, providing powerful decision-making support for power plant managers and helping to formulate more scientific and reasonable operation plans. Real-time monitoring and intelligent analysis help to detect safety hazards in a timely manner, take measures to prevent accidents from occurring, and improve the safety performance of power plants.

[0024] More specifically, a time series database can be used for compressed storage of high-frequency vibration data. It can be understood that using a time series database for compressed storage of high-frequency vibration data significantly reduces the demand for data storage space. The compression technology ensures data integrity while improving the efficiency of data storage and transmission. The time series database optimizes the data retrieval speed, making it possible to quickly locate and analyze high-frequency vibration data for a specific time period among a large amount of historical data. Fast retrieval helps to respond to equipment anomalies in a timely manner and shorten the fault diagnosis time. Through efficient data compression, the cost of long-term storage of high-frequency vibration data is reduced. The investment and maintenance costs of storage devices are reduced, improving the overall economy. The time series database supports complex data analysis operations such as trend analysis and anomaly detection. It improves the performance and accuracy of the digital twin model in processing and analyzing high-frequency vibration data. Combined with real-time data stream processing technology, the time series database can receive, compress, and store high-frequency vibration data in real time. It enhances the real-time synchronization between the digital twin model and the actual power plant equipment status and improves the response speed of the monitoring system. Efficient data compression and storage provide a large amount of high-quality training data for machine learning and artificial intelligence algorithms. It optimizes the training process of the digital twin model and improves the prediction accuracy and generalization ability of the model. The design of the time series database is specifically for the storage and query of time series data, improving the stability and reliability of the system. It reduces the risk of system failures or data loss caused by improper data management. Efficient compressed storage makes it feasible to accumulate high-frequency vibration data over a long period, providing data support for the long-term operation analysis and optimization of the power plant. It supports the research on the long-term operation trends of equipment and helps to discover potential long-term problems.

[0025] By using a time series database for compressed storage of high-frequency vibration data, this method for constructing a digital twin power plant has achieved significant improvements in aspects such as data management, analysis efficiency, and cost control, further enhancing the application value and effect of digital twin technology in power plant operation and maintenance management.

[0026] More specifically, the multiple sensors include at least two of temperature, pressure, flow rate, and vibration sensors. In this way, by integrating at least two of temperature, pressure, flow rate, and vibration sensors, multi-dimensional and comprehensive operation status data collection of power plant equipment is achieved. The multi-dimensional data provides richer information, which helps to more accurately analyze and judge the equipment status. By combining the data of multiple sensors, the causes and locations of equipment failures can be diagnosed more accurately. For example, by simultaneously analyzing temperature and vibration data, problems such as bearing failures or thermal imbalances can be identified more effectively. The data fusion of multiple sensors provides more comprehensive input features for machine learning and artificial intelligence algorithms, improving the accuracy of predictive maintenance. Potential problems can be detected earlier, maintenance can be carried out in advance, and unexpected shutdowns can be avoided. By analyzing the data of sensors such as temperature, pressure, and flow rate, the operation parameters of the equipment can be adjusted in real time to optimize the operation efficiency. For example, the operation speed of the pump can be adjusted according to the flow rate and pressure data to maximize energy efficiency. The real-time monitoring of multiple sensors helps to promptly discover safety hazards such as overheating and overpressure, and immediately take measures to enhance the safety of the power plant. The data provided by different sensors is complementary and can be mutually verified to reduce false alarms and missed alarms. For example, the abnormal vibration detected by the vibration sensor can be further confirmed by the data of the temperature sensor to determine whether it is caused by overheating. Based on the data of multiple sensors, the comprehensive performance evaluation of power plant equipment can be realized. The comprehensive performance evaluation helps to formulate more reasonable maintenance and upgrade plans. Through the data analysis and predictive maintenance of multiple sensors, unnecessary maintenance operations can be reduced, and the maintenance cost can be lowered. The situations of over-maintenance or under-maintenance are avoided, and the maintenance efficiency is improved. The data fusion of multiple sensors makes the digital twin model more realistic and more accurately reflects the operation status of the actual equipment. It improves the application value of the digital twin model in simulation, analysis, and optimization.

[0027] More specifically, edge computing nodes can be deployed on the device side to achieve data cleaning and feature extraction. Deploying edge computing nodes on the device side enables real-time cleaning and preprocessing of the collected data, removing noise, outliers, and redundant information. Real-time data cleaning ensures the quality of the data for subsequent analysis, improving the effectiveness and accuracy of the data. Edge computing nodes perform data cleaning and feature extraction on the device side, reducing the amount of data that needs to be transmitted to the cloud. Reducing the data transmission burden helps alleviate network pressure and improve data transmission efficiency. Edge computing nodes can quickly extract key features, providing directly usable inputs for the digital twin model. Fast feature extraction accelerates the data processing flow and shortens the response time from data collection to model application. Processing data on the device side reduces the risk of data exposure during transmission. Enhancing data security protects the sensitive information and intellectual property rights of the power plant. Edge computing nodes provide distributed computing capabilities, sharing the computing pressure on the cloud platform. Distributed computing improves the overall system's processing capacity, supporting the access of more devices and more complex data analysis. Edge computing nodes can detect the device status in real time and quickly respond to potential failures. Improving the fault response speed reduces the impact of faults on the operation of the power plant. By performing data cleaning and feature extraction on the device side, the utilization of computing resources is optimized. Avoiding unnecessary resource waste improves resource utilization efficiency. Edge computing nodes can continue to perform data cleaning and feature extraction in the case of unstable or disconnected networks. Supporting offline operation enhances the robustness and reliability of the system. The data preprocessed on the device side is directly transmitted to the cloud, simplifying the processing flow in the cloud. Simplifying the process improves the efficiency and accuracy of cloud processing. The real-time data cleaning and feature extraction functions provided by edge computing nodes enhance the real-time nature of the digital twin model. Enabling the digital twin model to be more closely synchronized with the actual device status improves the accuracy of simulation and prediction.

[0028] More specifically, by deploying a data quality monitoring module, abnormal acquisition nodes can be automatically identified. Deploying a data quality monitoring module can automatically identify abnormal acquisition nodes without manual intervention. It can detect abnormalities in real time to ensure the accuracy and reliability of data. By automatically identifying and excluding abnormal data, the overall data quality can be significantly improved. It provides a more accurate and reliable data foundation for the digital twin model. Automated anomaly detection reduces the workload of manual inspection and data cleaning. It reduces labor costs and improves data processing efficiency. Timely identification and isolation of abnormal acquisition nodes prevent incorrect data from affecting the normal operation of the system. It enhances the robustness and stability of the system. The data quality monitoring module can quickly locate the source of anomalies, facilitating timely repair or replacement of faulty equipment. It shortens the fault recovery time and reduces the impact on power plant operation. After excluding abnormal data, the data fusion process becomes smoother and the results are more accurate. It improves data fusion efficiency and optimizes the data processing flow. The digital twin model built based on high-quality data has higher accuracy. It enhances the effects of model simulation, prediction, and optimization decision-making. High-quality data and accurate model results enhance confidence in decision-making. It is conducive to formulating more scientific and reasonable operation strategies. Automatically identifying abnormal acquisition nodes helps prevent potential data errors and system risks. Taking measures in advance can avoid accidents. The data quality monitoring module is applicable to the deployment of large-scale sensor networks. It supports the access of more devices and expands the system scale.

[0029] More specifically, version tracing of the digital twin model can be achieved by constructing a digital thread. It can be understood that the digital thread records the complete history of the digital twin model from creation to each modification, realizing precise version control of the model. It is convenient for users to view, compare, and trace back to any historical version, ensuring the continuity and consistency of the model. Through the digital thread, all changes to the model can be clearly traced, including information such as the change time, change content, and change personnel. It improves the traceability of the model and helps with auditing and compliance checks. The version tracing function makes the model iteration process more transparent and orderly.

[0030] It also facilitates team collaboration, allowing multiple people to work on different versions simultaneously and finally merging and optimizing the results. When problems occur in the model, the problem version can be quickly located through version tracing. It can quickly restore to a stable version, reducing the impact of faults on the power plant operation. By continuously tracking and recording model changes, the quality and reliability of the model are ensured. The accumulation of problems caused by un-tracked changes is avoided. It provides a complete view of the model evolution, providing strong support for management decision-making. Based on historical data and analysis, more scientific and reasonable operation strategies are formulated. The digital thread records the whole process of model construction and optimization, forming valuable knowledge assets. It is conducive to knowledge sharing and inheritance, and improves the overall skill level of the team. Through version tracing, the chaos and repetitive work caused by model changes are reduced. The model maintenance cost is reduced, and the work efficiency is improved. The clear version history enables excellent models to be easily reused and promoted. The reusability of the model is improved, saving development resources. The digital thread provides a basis for continuous improvement of the model. By analyzing historical versions, the model performance and accuracy are continuously optimized.

[0031] More specifically, through intelligent algorithms, the operating status of equipment is analyzed and predicted, providing decision-making suggestions for power plant managers on optimizing operation, preventive maintenance, and upgrading. Based on the analysis results of intelligent algorithms, the operating parameters of equipment are adjusted in real time to ensure that the equipment always operates in the best state, improving energy efficiency and power generation. Through intelligent optimization, unnecessary energy waste is reduced, achieving energy conservation and emission reduction. Intelligent algorithms can predict potential faults and wear conditions of equipment, issue early warnings in advance, and avoid downtime losses caused by sudden failures. By regularly performing preventive maintenance, the service life of equipment is effectively extended, and the replacement frequency and cost are reduced. Based on the in-depth analysis of equipment operation data by intelligent algorithms, a scientific and data-driven decision-making basis for upgrading is provided. Through prediction models, the return on investment of different upgrading solutions is evaluated to help managers select the most economical solution. Intelligent algorithms replace traditional manual experience judgment, reducing human errors and improving the accuracy and reliability of decision-making. Considering multiple factors for decision analysis ensures the comprehensiveness and rationality of decisions. Intelligent algorithms can identify potential risks in equipment operation, take timely measures, and enhance the safety management of power plants. By prediction and preventive maintenance, the probability of accidents is reduced, ensuring the safety of personnel and equipment. Intelligent algorithms achieve automatic monitoring and adjustment of equipment operation, reducing manual intervention and improving operation efficiency. According to the equipment operation status and prediction results, resource allocation is optimized to improve resource utilization efficiency. Preventive maintenance reduces the need and cost of sudden repairs. Based on the predicted spare part requirements, inventory management is optimized to reduce spare part storage costs. Adopting advanced intelligent algorithm technologies improves the technical level and market competitiveness of power plants. Based on the decision-making support of intelligent analysis, power plants can more flexibly respond to market changes and demands.

[0032] More specifically, the digital twin model can be used to manage the entire life cycle of power plant equipment.

[0033] In this way, during the equipment design phase, the digital twin model can serve as a virtual prototype to help designers conduct comprehensive simulations and analyses of the equipment in a virtual environment. This can not only identify potential design problems in advance, such as insufficient structural strength and excessive thermal deformation, but also predict and optimize the performance of the equipment. Through digital twin technology, designers can simulate the operation of the equipment under different working conditions, including cutting force, vibration, temperature distribution, etc., thus ensuring the reliability and effectiveness of the design scheme. During the equipment manufacturing and installation phase, the digital twin model can be used as production guidance to help manufacturers achieve intelligent and visual production processes. By constructing digital twin models of production equipment and production lines and collecting real-time operation data and production data of the equipment, enterprises can comprehensively monitor and optimize the production process. This can not only improve production efficiency but also ensure the consistency of product quality. At the same time, the digital twin model can also be used for virtual commissioning of equipment, reducing the commissioning cost and cycle and improving the safety and accuracy of commissioning. During the equipment operation phase, the digital twin model can serve as a real-time monitoring and early warning system to help power plants implement preventive maintenance of equipment. By real-time monitoring the operation status and performance data of the equipment, the digital twin model can predict when the equipment may fail and issue early warnings. This can not only reduce unexpected downtime and maintenance costs but also improve the reliability and stability of the equipment. In addition, the digital twin model can also be used for remote monitoring and support of equipment. Regardless of the geographical location, engineers can view the status information of the equipment in real time through the digital twin platform for remote diagnosis and support. During the equipment maintenance phase, the digital twin model can provide maintenance plans and optimization suggestions. By recording the maintenance history and performance data of the equipment, the digital twin model can analyze the maintenance requirements and cycles of the equipment and formulate corresponding maintenance plans. This can not only reduce maintenance costs but also improve the utilization rate and reliability of the equipment. At the same time, the digital twin model can also be used for fault diagnosis and elimination of equipment. By simulating and analyzing the operation status of the equipment, the fault point can be quickly located and solutions can be provided. During the equipment retirement phase, the digital twin model can serve as an evaluation tool to help power plants formulate scientific scrapping plans. By recording the entire life cycle data of the equipment, including usage frequency, maintenance history, etc., the digital twin model can evaluate the remaining life and performance status of the equipment and provide reasonable scrapping suggestions for power plants. This can not only avoid resource waste caused by prematurely scrapping equipment that still has use value but also prevent potential safety hazards and increased operating costs brought about by delaying the scrapping of equipment that can no longer be used normally.

[0034] More specifically, a safety monitoring system can be established to monitor and warn of 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, ensuring that any abnormal situation can be captured in a timely manner. Once a potential safety threat is detected, the system immediately issues a warning to notify relevant personnel to take response measures promptly to prevent accidents. Machine learning and artificial intelligence technologies are used to analyze the collected data to predict potential safety risks and achieve early prevention. Through early warning and intervention, the occurrence of power plant accidents can be effectively reduced, ensuring the safety of personnel and equipment. The safety monitoring system provides real-time data support, enabling the emergency response team to quickly understand the accident situation and formulate effective emergency measures. The system supports multi-department collaborative operations, improving the efficiency and effectiveness of emergency response. The safety monitoring system provides comprehensive and accurate data support to help managers make scientific and reasonable decisions. Based on the analysis results of the data, safety strategies and measures are optimized to improve the overall safety level of the power plant. Through real-time monitoring and warning, precise maintenance is achieved, avoiding over-maintenance or under-maintenance, and reducing maintenance costs. Problems are discovered and handled in a timely manner, extending the service life of equipment and reducing replacement costs. The establishment and operation of the safety monitoring system help to enhance the safety awareness of all employees. Promote the construction of the power plant safety culture, forming a good atmosphere where everyone pays attention to safety and participates in safety. Establishing a safety monitoring system meets the requirements of relevant regulations and standards, ensuring the compliance operation of the power plant. Through the monitoring and warning of the system, legal risks and compensation liabilities caused by safety accidents are avoided. The operation status of the safety monitoring system can be made public externally, enhancing the transparency of the power plant and strengthening public trust. By ensuring the safety of the power plant, fulfilling corporate social responsibilities, and establishing a good corporate image.

[0035] More specifically, an equipment health management system can be established to achieve preventive maintenance and fault prediction of equipment in combination with a digital twin model. The equipment health management system is a comprehensive management platform that integrates various sensors, data analysis algorithms, and prediction models for real-time monitoring and analysis of the operating status of power plant equipment. The core objective of this system is to detect potential faults in equipment in advance and implement preventive maintenance, thereby reducing maintenance costs and improving the reliability and stability of the equipment. The digital twin model can reflect the operating status of power plant equipment in real time, including key parameters such as temperature, pressure, and vibration. By collecting and analyzing this real-time data, the system can promptly detect abnormal conditions or potential faults in the equipment. Based on the prediction results of the digital twin model, the power plant can formulate a maintenance plan in advance and conduct preventive maintenance on the equipment. This can not only reduce unexpected downtime but also extend the service life of the equipment and lower maintenance costs. The digital twin model can simulate the operating status of the equipment and predict the types and times of possible faults. When a fault occurs in the equipment, the system can quickly locate the fault point and provide corresponding repair solutions. The system collects real-time data through various sensors installed on power plant equipment. This data is transmitted to the central processing unit through a communication network for analysis and processing. The central processing unit uses advanced algorithms and models to analyze the collected data. By comparing the status of the digital twin model with that of the actual equipment, the system can predict the health status and potential faults of the equipment. Based on the prediction results, the system can automatically generate a maintenance plan, including maintenance time, maintenance content, and required spare parts, etc. These plans can be updated in real time to ensure consistency with the actual status of the equipment. When a fault occurs in the equipment, the system can quickly locate the fault point and provide corresponding repair solutions. Maintenance personnel can carry out troubleshooting work according to the guidance provided by the system, improving the maintenance efficiency.

[0036] In this way, through preventive maintenance and fault prediction, the system can significantly reduce the failure rate of equipment and improve the reliability and stability of the equipment. By formulating a maintenance plan in advance and quickly locating the fault point, the system can reduce maintenance costs and time costs. The implementation of the equipment health management system can optimize the production process of the power plant, improve production efficiency and quality. Through real-time monitoring and early warning of equipment, the system can enhance the safety management level of the power plant and reduce the occurrence of safety accidents.

[0037] Compared with the prior art, a method for constructing a digital twin power plant provided by an embodiment of the present invention fuses multi-source data through a cloud computing platform, which 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 the digital twin model. Through real-time data stream processing technology, the high synchronization between the digital twin model and the actual power plant equipment status is ensured. This real-time nature ensures that the model can accurately reflect the latest status of the equipment, providing a reliable basis for the real-time management and decision-making of the power plant. The adaptive optimization algorithm can automatically adjust the parameters of the digital twin model according to real-time data feedback, improving the prediction accuracy and adaptability of the model.

[0038] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0039] It should be understood that various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. No limitation is imposed herein.

[0040] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a digital twin power plant, characterized in that: include: Use a variety of sensors to collect data on the operating status of power plant equipment in real time, and integrate multi-source data through the cloud computing platform to form a unified data format; Based on the fused holographic data, a digital twin model is constructed using machine learning and artificial intelligence technologies; Through real-time data stream processing technology, the digital twin model is synchronized with the actual power plant equipment status; Adaptive optimization algorithms are used to adjust the parameters of the digital twin model based on real-time data feedback.

2. The method for constructing a digital twin power plant according to claim 1, characterized in that: A time series database is used to compress and store high-frequency vibration data.

3. The method for constructing a digital twin power plant according to claim 1, characterized in that: The plurality of sensors include at least two of temperature, pressure, flow and vibration sensors.

4. The method for constructing a digital twin power plant according to claim 1, characterized in that: Deploy edge computing nodes on the device side to achieve data cleaning and feature extraction.

5. The method for constructing a digital twin power plant according to claim 1, characterized in that: By deploying the data quality monitoring module, abnormal collection nodes can be automatically identified.

6. The method for constructing a digital twin power plant according to claim 1, characterized in that: Realize version traceability of digital twin models by building a digital thread.

7. The method for constructing a digital twin power plant according to claim 1, characterized in that: Through intelligent algorithms, the operating status of equipment is analyzed and predicted, providing power plant managers with decision-making recommendations for optimized operation, preventive maintenance, and upgrades.

8. The method for constructing a digital twin power plant according to claim 1, characterized in that: The entire life cycle of power plant equipment is managed through digital twin models.

9. The method for constructing a digital twin power plant according to claim 1, characterized in that: Establish a security monitoring system to monitor and warn of potential security threats in real time.

10. The method for constructing a digital twin power plant according to claim 1, characterized in that: Establish an equipment health management system and combine it with the digital twin model to realize preventive maintenance and fault prediction of equipment.

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