An operation monitoring method and system based on digital twin technology

Abstract

The present invention discloses an operation monitoring method and system based on digital twin technology, which relates to the field of digital twin technology. The method includes: constructing a digital twin model that can reflect the operating status and performance parameters of the actual operation system in real time; deploying sensors and monitoring equipment to collect the operating data of the actual operation system in real time and perform preprocessing; comparing the operating data of the actual operation system with that of the digital twin model in real time, monitoring the difference in the operating data of the two, and when the difference exceeds a preset threshold, triggering an early warning mechanism to notify the operation personnel to handle it; using the digital twin model to perform simulation, predicting the operating status and performance of the actual operation system in a set time period in the future, and optimizing and adjusting the actual operation system in combination with operation goals and needs. By constructing a digital twin model and combining real-time data collection and integration, the present invention can achieve real-time monitoring, prediction and optimization of the actual operation system, thereby improving operation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and more specifically to an operation monitoring method and system based on digital twin technology. Background Art

[0002] With the continuous development of information technology and intelligent technologies, the complexity and diversity of operational systems are increasing, placing higher demands on operational monitoring. Traditional methods, limited by manual inspections and data analysis, are unable to meet the monitoring needs of modern operational systems. Therefore, a new operational monitoring method and system is needed that can achieve comprehensive, accurate, and real-time monitoring of actual operational systems. Summary of the Invention

[0003] In response to the needs and shortcomings of current technological development, the present invention provides an operation monitoring method and system based on digital twin technology. By constructing a digital twin model corresponding to the actual operation system and combining real-time data collection and integration, real-time monitoring, prediction and optimization of the operating status of the actual operation system can be achieved, thereby improving operational efficiency and decision-making accuracy.

[0004] In the first aspect, the present invention provides an operation monitoring method based on digital twin technology. The technical solutions adopted to solve the above technical problems are as follows:

[0005] An operation monitoring method based on digital twin technology includes the following steps:

[0006] S1. Build a digital twin model that can reflect the operating status and performance parameters of the actual operating system in real time;

[0007] S2. Deploy sensors and monitoring equipment to collect and pre-process the operating data of the actual operating system in real time;

[0008] S3. Compare the operating data of the actual operating system and the digital twin model in real time, monitor the difference in operating data between the actual operating system and the digital twin model, and trigger an early warning mechanism when the difference exceeds the preset threshold to notify the operating personnel to handle it.

[0009] Optionally, the operational monitoring methods involved also include:

[0010] S4. Use digital twin models for simulation to predict the operating status and performance of the actual operating system within a set time period in the future, and optimize and adjust the actual operating system based on operational goals and needs.

[0011] Optionally, execute step S1 to build a digital twin model. The specific operations include:

[0012] S1.1. Understand the functions and structure of the actual operating system and collect the operational and performance data of the actual operating system;

[0013] S1.2. Collect historical data from the actual operating system, including monitoring data, sensor data, and operation logs, and pre-process the collected data;

[0014] S1.3. Build a digital twin model based on the actual operating principles and behaviors of the actual operating system. Use principles of physics, chemistry, biology, or engineering to describe the dynamic behavior of the actual operating system and determine the input-output relationships, parameters, and variables of the digital twin model.

[0015] S1.4. Verify the digital twin model using real-time data from the actual operating system to ensure its accuracy; adjust the model parameters of the digital twin model so that the digital twin model output matches the behavior of the actual operating system;

[0016] S1.5. Integrate the digital twin model with real-time data from the actual operating system to achieve dynamic data updates. At the same time, develop an interface to enable the digital twin model to communicate with the actual operating system.

[0017] S1.6. Design a user-friendly interface for the digital twin model to allow operators to monitor and control the digital twin model, while also enabling data visualization to help users understand the current operating status of the actual operating system and predict the behavior of the actual operating system within a specified time period in the future;

[0018] S1.7. Test the performance and accuracy of the digital twin model in the actual operational system and adjust and optimize the digital twin model based on the test results. Deploy the digital twin model to the production environment and regularly update and maintain the model to ensure it is synchronized with the actual operational system.

[0019] S1.8. Establish a feedback mechanism to enable the digital twin model to learn from the operation of the actual operating system and continuously optimize itself.

[0020] Optionally, deploy sensors and monitoring equipment to collect real-time operating data of the actual operating system, including user operations on the equipment, equipment operating status, and environmental parameters of the equipment, and perform pre-processing operations such as data cleaning, integration, and formatting on the real-time collected data.

[0021] In a second aspect, the present invention provides an operation monitoring system based on digital twin technology. The technical solutions adopted to solve the above technical problems are as follows:

[0022] An operation monitoring system based on digital twin technology, comprising:

[0023] The model building module is used to build a digital twin model that can reflect the operating status and performance parameters of the actual operating system in real time;

[0024] The data collection module is used to collect the operating data of the actual operation system in real time through sensors and monitoring equipment deployed in the actual operation system;

[0025] A preprocessing module, used for preprocessing the collected data of the data collection module;

[0026] The threshold setting module is used to set the difference threshold between the operating data of the actual operating system and the digital twin model;

[0027] The monitoring and early warning module is used to compare the operating data of the actual operating system and the digital twin model in real time, monitor the differences in the operating data between the actual operating system and the digital twin model, and trigger the early warning mechanism when the difference exceeds the set difference threshold to notify the operating personnel to handle it.

[0028] Optionally, the operation monitoring system involved also includes:

[0029] The prediction and optimization module is used to use the digital twin model for simulation to predict the operating status and performance of the actual operating system within a set time period in the future, and to optimize and adjust the actual operating system based on operational goals and needs.

[0030] Optionally, the specific operations taken by the model construction module to build the digital twin model are as follows:

[0031] (1) Understand the functions and structure of the actual operating system and collect the operation data and performance data of the actual operating system;

[0032] (2) Collect historical data of the actual operating system, including monitoring data, sensor data, and operation logs, and pre-process the collected data;

[0033] (3) Establish a digital twin model based on the actual working principles and behaviors of the actual operating system. At the same time, use the principles of physics, chemistry, biology or engineering to describe the dynamic behavior of the actual operating system, and then determine the input-output relationship, parameters and variables of the digital twin model;

[0034] (4) Use real-time data from the actual operating system to verify the digital twin model to ensure its accuracy; adjust the model parameters of the digital twin model so that the output of the digital twin model matches the behavior of the actual operating system;

[0035] (5) Integrate the digital twin model with the real-time data of the actual operation system to achieve dynamic data update. At the same time, develop an interface to enable the digital twin model to communicate with the actual operation system;

[0036] (6) Design a user-friendly interface for the digital twin model so that operators can monitor and control the digital twin model, and realize data visualization to help users understand the current operating status of the actual operating system and predict the behavior of the actual operating system in the future specified time period;

[0037] (7) Test the performance and accuracy of the digital twin model in the actual operating system, and adjust and optimize the digital twin model based on the test results; deploy the digital twin model to the production environment, and regularly update and maintain the model to ensure its synchronization with the actual operating system;

[0038] (8) Establish a feedback mechanism to enable the digital twin model to learn from the operation of the actual operating system and continuously optimize itself.

[0039] Optionally, the data acquisition module collects real-time operating data of the actual operating system through sensors and monitoring devices deployed in the actual operating system, including user operations on the device, device operating status, and environmental parameters of the device;

[0040] The preprocessing module involved performs preprocessing operations such as data cleaning, integration and formatting on the collected data of the data acquisition module.

[0041] The operation monitoring method and system based on digital twin technology of the present invention have the following beneficial effects compared with the existing technology:

[0042] 1. By constructing a digital twin model corresponding to the actual operating system and combining it with real-time data collection and integration, the present invention can achieve real-time monitoring, prediction and optimization of the actual operating system, thereby improving operational efficiency and decision-making accuracy.

[0043] 2. This invention uses digital twin technology and real-time data collection to achieve comprehensive monitoring of actual operating systems, timely identify potential risks, and improve operational efficiency. It uses digital twin models for simulation and prediction to provide a scientific basis for operational decision-making, and at the same time, adjusts based on optimization goals to improve operational quality.

[0044] 3. In practical applications, this invention can be combined with cloud computing, big data and other technologies to improve the efficiency of data processing and analysis. At the same time, the accuracy and reliability of the digital twin model can be ensured by regularly updating and improving the digital twin model. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Attachment Figure 1is a flow chart of a method according to embodiment 1 of the present invention;

[0046] Attachment Figure 2 This is a module connection block diagram of the second embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the technical solution, the technical problems solved and the technical effects of the present invention more clear, the technical solution of the present invention is clearly and completely described below in conjunction with specific embodiments.

[0048] Example 1:

[0049] Combined with attachment Figure 1 This embodiment proposes an operation monitoring method based on digital twin technology, which includes the following steps:

[0050] S1. Build a digital twin model that can reflect the operating status and performance parameters of the actual operating system in real time.

[0051] The specific operations for building a digital twin model are as follows:

[0052] S1.1. Understand the functions and structure of the actual operating system and collect the operational and performance data of the actual operating system;

[0053] S1.2. Collect historical data from the actual operating system, including monitoring data, sensor data, and operation logs, and pre-process the collected data;

[0054] S1.3. Build a digital twin model based on the actual operating principles and behaviors of the actual operating system. Use principles of physics, chemistry, biology, or engineering to describe the dynamic behavior of the actual operating system and determine the input-output relationships, parameters, and variables of the digital twin model.

[0055] S1.4. Verify the digital twin model using real-time data from the actual operating system to ensure its accuracy; adjust the model parameters of the digital twin model so that the digital twin model output matches the behavior of the actual operating system;

[0056] S1.5. Integrate the digital twin model with real-time data from the actual operating system to achieve dynamic data updates. At the same time, develop an interface to enable the digital twin model to communicate with the actual operating system.

[0057] S1.6. Design a user-friendly interface for the digital twin model to allow operators to monitor and control the digital twin model, while also enabling data visualization to help users understand the current operating status of the actual operating system and predict the behavior of the actual operating system within a specified time period in the future;

[0058] S1.7. Test the performance and accuracy of the digital twin model in the actual operational system and adjust and optimize the digital twin model based on the test results. Deploy the digital twin model to the production environment and regularly update and maintain the model to ensure it is synchronized with the actual operational system.

[0059] S1.8. Establish a feedback mechanism to enable the digital twin model to learn from the operation of the actual operating system and continuously optimize itself.

[0060] S2. Deploy sensors and monitoring equipment to collect real-time operational data from the actual operating system, including user operations on the equipment, equipment operating status, and environmental parameters of the equipment;

[0061] Perform pre-processing operations such as data cleaning, integration and formatting on real-time collected data.

[0062] S3. Compare the operating data of the actual operating system and the digital twin model in real time, monitor the difference in operating data between the actual operating system and the digital twin model, and trigger an early warning mechanism when the difference exceeds the preset threshold to notify the operating personnel to handle it.

[0063] S4. Use digital twin models for simulation to predict the operating status and performance of the actual operating system within a set time period in the future, and optimize and adjust the actual operating system based on operational goals and needs.

[0064] Example 2:

[0065] Combined with attachment Figure 2 This embodiment proposes an operation monitoring system based on digital twin technology, which includes:

[0066] The model building module is used to build a digital twin model that can reflect the operating status and performance parameters of the actual operating system in real time;

[0067] The data collection module is used to collect real-time operating data of the actual operating system through sensors and monitoring devices deployed in the actual operating system, including user operations on the equipment, equipment operating status, and environmental parameters of the equipment;

[0068] The preprocessing module is used to perform preprocessing operations such as data cleaning, integration and formatting on the data collected by the data acquisition module;

[0069] The threshold setting module is used to set the difference threshold between the operating data of the actual operating system and the digital twin model;

[0070] The monitoring and early warning module is used to compare the operating data of the actual operating system with the digital twin model in real time, monitor the difference between the operating data of the actual operating system and the digital twin model, and trigger the early warning mechanism when the difference exceeds the set difference threshold, notifying the operation personnel to handle it;

[0071] The prediction and optimization module is used to use the digital twin model for simulation to predict the operating status and performance of the actual operating system within a set time period in the future, and to optimize and adjust the actual operating system based on operational goals and needs.

[0072] In this embodiment, the specific operations taken by the model construction module to construct the digital twin model are as follows:

[0073] (1) Understand the functions and structure of the actual operating system and collect the operation data and performance data of the actual operating system;

[0074] (2) Collect historical data of the actual operating system, including monitoring data, sensor data, and operation logs, and pre-process the collected data;

[0075] (3) Establish a digital twin model based on the actual working principles and behaviors of the actual operating system. At the same time, use the principles of physics, chemistry, biology or engineering to describe the dynamic behavior of the actual operating system, and then determine the input-output relationship, parameters and variables of the digital twin model;

[0076] (4) Use real-time data from the actual operating system to verify the digital twin model to ensure its accuracy; adjust the model parameters of the digital twin model so that the output of the digital twin model matches the behavior of the actual operating system;

[0077] (5) Integrate the digital twin model with the real-time data of the actual operation system to achieve dynamic data updates. At the same time, develop an interface to enable the digital twin model to communicate with the actual operation system;

[0078] (6) Design a user-friendly interface for the digital twin model so that operators can monitor and control the digital twin model, and realize data visualization to help users understand the current operating status of the actual operating system and predict the behavior of the actual operating system in the future specified time period;

[0079] (7) Test the performance and accuracy of the digital twin model in the actual operating system, and adjust and optimize the digital twin model based on the test results; deploy the digital twin model to the production environment, and regularly update and maintain the model to ensure its synchronization with the actual operating system;

[0080] (8) Establish a feedback mechanism to enable the digital twin model to learn from the operation of the actual operating system and continuously optimize itself.

[0081] In summary, the operation monitoring method and system based on digital twin technology of the present invention can realize real-time monitoring, prediction and optimization of the actual operation system by constructing a digital twin model corresponding to the actual operation system and combining real-time data collection and integration, thereby improving operation efficiency and decision-making accuracy.

[0082] The above specific examples are used to illustrate the principles and implementation methods of the present invention in detail. These examples are only used to help understand the core technical content of the present invention. Based on the above specific embodiments of the present invention, any improvements and modifications made by those skilled in the art without departing from the principles of the present invention should fall within the scope of patent protection of the present invention.

Claims

1. An operation monitoring method based on digital twin technology, characterized in that: The steps include: S1. Build a digital twin model that can reflect the operating status and performance parameters of the actual operating system in real time. Specific operations include: S1.

1. Understand the functions and structure of the actual operating system and collect the operational and performance data of the actual operating system; S1.

2. Collect historical data from the actual operating system, including monitoring data, sensor data, and operation logs, and pre-process the collected data; S1.

3. Build a digital twin model based on the actual operating principles and behaviors of the actual operating system. Use principles of physics, chemistry, biology, or engineering to describe the dynamic behavior of the actual operating system and determine the input-output relationships, parameters, and variables of the digital twin model. S1.

4. Verify the digital twin model using real-time data from the actual operating system to ensure its accuracy; adjust the model parameters of the digital twin model so that the digital twin model output matches the behavior of the actual operating system; S1.

5. Integrate the digital twin model with real-time data from the actual operating system to achieve dynamic data updates. At the same time, develop an interface to enable the digital twin model to communicate with the actual operating system. S1.

6. Design a user-friendly interface for the digital twin model to allow operators to monitor and control the digital twin model, while also enabling data visualization to help users understand the current operating status of the actual operating system and predict the behavior of the actual operating system within a specified time period in the future; S1.

7. Test the performance and accuracy of the digital twin model in the actual operational system and adjust and optimize the digital twin model based on the test results. Deploy the digital twin model to the production environment and regularly update and maintain the model to ensure it is synchronized with the actual operational system. S1.

8. Establish a feedback mechanism to enable the digital twin model to learn from the operation of the actual operating system and continuously optimize itself; S2. Deploy sensors and monitoring equipment to collect and pre-process the operating data of the actual operating system in real time; S3. Compare the operating data of the actual operating system and the digital twin model in real time, monitor the difference in operating data between the actual operating system and the digital twin model, and trigger an early warning mechanism when the difference exceeds the preset threshold to notify the operating personnel to handle it.

2. The operation monitoring method based on digital twin technology according to claim 1, characterized in that: The method further comprises: S4. Use digital twin models for simulation to predict the operating status and performance of the actual operating system within a set time period in the future, and optimize and adjust the actual operating system based on operational goals and needs.

3. The operation monitoring method based on digital twin technology according to claim 1, characterized in that: Deploy sensors and monitoring equipment to collect real-time operating data of the actual operating system, including user operations on the equipment, equipment operating status and environmental parameters of the equipment, and perform pre-processing operations such as data cleaning, integration and formatting on the real-time collected data.

4. An operation monitoring system based on digital twin technology, characterized in that: It includes: The model building module is used to build a digital twin model that can reflect the operating status and performance parameters of the actual operation system in real time. The specific operations taken by the model building module to build the digital twin model are as follows: (1) understanding the functions and structure of the actual operation system and collecting the operation data and performance data of the actual operation system; (2) collecting historical data of the actual operation system, including monitoring data, sensor data and operation logs, and preprocessing the collected data; (3) Establish a digital twin model based on the actual working principles and behaviors of the actual operating system. At the same time, use the principles of physics, chemistry, biology or engineering to describe the dynamic behavior of the actual operating system, and then determine the input-output relationship, parameters and variables of the digital twin model; (4) Use the real-time data of the actual operating system to verify the digital twin model to ensure the accuracy of the digital twin model; adjust the model parameters of the digital twin model so that the output of the digital twin model matches the behavior of the actual operating system; (5) Integrate the digital twin model with the real-time data of the actual operating system to realize dynamic data update. At the same time, develop an interface to enable the digital twin model to communicate with the actual operating system; (6) Design a user-friendly interface for the digital twin model so that operators can monitor and control the digital twin model, and realize data visualization to help users understand the current operating status of the actual operating system and predict the behavior of the actual operating system in the future specified time period; (7) Test the performance and accuracy of the digital twin model in the actual operating system, and adjust and optimize the digital twin model based on the test results; deploy the digital twin model to the production environment, and regularly update and maintain the model to ensure its synchronization with the actual operating system; (8) Establish a feedback mechanism so that the digital twin model can learn from the operation of the actual operating system and continuously optimize itself; The data collection module is used to collect the operating data of the actual operation system in real time through sensors and monitoring equipment deployed in the actual operation system; A preprocessing module, used for preprocessing the collected data of the data collection module; The threshold setting module is used to set the difference threshold between the operating data of the actual operating system and the digital twin model; The monitoring and early warning module is used to compare the operating data of the actual operating system and the digital twin model in real time, monitor the differences in the operating data between the actual operating system and the digital twin model, and trigger the early warning mechanism when the difference exceeds the set difference threshold to notify the operating personnel to handle it.

5. The operation monitoring system based on digital twin technology according to claim 4 is characterized in that: The system further comprises: The prediction and optimization module is used to use the digital twin model for simulation to predict the operating status and performance of the actual operating system within a set time period in the future, and to optimize and adjust the actual operating system based on operational goals and needs.

6. The operation monitoring system based on digital twin technology according to claim 4 is characterized in that: The data acquisition module collects the operating data of the actual operating system in real time through sensors and monitoring devices deployed in the actual operating system, including user operations on the equipment, equipment operating status and environmental parameters of the equipment; The pre-processing module performs pre-processing operations of data cleaning, integration and formatting on the data collected by the data collection module.

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