Detailed analysis of the construction method of digital twins
1. Introduction
Under the strong impact of the digital wave, digital twin technology is like a bright new star, integrating into various industries at an unimaginable speed, and becoming the core driving force for promoting industrial upgrading and innovative development. The uniqueness of this technology is that it can build a mapping model in virtual space that is almost perfectly matched with the physical product. For example, in the field of aerospace, by digital twin modeling of aircraft engines, engineers can monitor the changes in parameters such as temperature and pressure inside the engine in real time, accurately predict potential failures, and arrange maintenance in advance, greatly improving the safety and reliability of aircraft operation.
The project construction of digital twin technology has similarities with traditional software projects, and both need to follow a rigorous and scientific process. From the initial demand research to the final system operation and maintenance, each link is closely connected and interlocking, and any omission in any link may affect the success or failure of the entire project. Only by ensuring that every step is solidly advanced can the project be smoothly implemented and efficient and stable operation be achieved.
II. Project Construction Process
(I) Demand Analysis
Clearly define the user scope: It is necessary to clearly define the user groups served by the project, including grassroots employees who are on the front line of production and directly operate the equipment, as well as senior managers who are responsible for making strategic decisions. Taking automobile manufacturing companies as an example, front-line workers may pay more attention to the real-time operation status and operation convenience of the equipment; while senior managers pay more attention to macro-level functions such as summary analysis of production data and capacity forecasting. Only by accurately grasping the differences in the needs of users at different levels can we create a digital twin system that truly meets the needs of various users.
Determine the business scope: It is crucial to accurately define the business areas involved in the project. For the manufacturing industry, its business scope not only includes core links such as raw material procurement, parts processing and assembly, and product quality testing, but also extends to upstream and downstream supply chain collaboration, logistics distribution and other related processes. For example, in the manufacturing of electronic products, the timely supply of raw materials directly affects the production progress. Through real-time monitoring and simulation of the supply chain through the digital twin system, the risk of raw material shortage can be predicted in advance, and the procurement plan can be adjusted in time to ensure the continuity of production.
Digging out pain points and difficulties: In-depth research on the bottlenecks and challenges encountered by users in existing business processes is the key. In many manufacturing companies, there is a common problem of difficulty in improving production efficiency. The reasons may be aging equipment, unreasonable process flow, etc.; equipment fault diagnosis is often time-consuming and labor-intensive, affecting production progress; high equipment maintenance costs are a major burden for enterprises. These pain points will become an important basis for the subsequent design of digital twin system functions. For example, in response to the difficulty of equipment fault diagnosis, the digital twin system can collect equipment operation data in real time and use data analysis algorithms to warn of potential faults in advance, thereby buying valuable time for maintenance personnel.
(II) Technical architecture
Adapting project scale and cost: Carefully select the appropriate technical architecture based on the scale of the project, the expected investment cost, and future expansion needs. For small projects, considering the limited resources and the need for rapid launch, a relatively simple and cost-controllable architecture can be adopted, such as a lightweight architecture based on a cloud computing platform, which can quickly build a system and realize basic functions. For large and complex projects, such as digital twin projects in smart city construction, it is necessary to process massive amounts of urban operation data and complex business logic, which requires the use of a highly scalable and stable distributed architecture to cope with the growing amount of data and functional requirements in the future.
Determine the system deployment environment: Combined with the actual situation of the enterprise, such as data security requirements, the current status of IT infrastructure and other factors, flexibly choose local deployment, cloud deployment or hybrid deployment. For financial enterprises with extremely high data security requirements, they may prefer local deployment to ensure the absolute security of data; while some start-ups, due to limited funds and high flexibility requirements, may choose cloud deployment to reduce the cost of hardware investment. Hybrid deployment is suitable for some enterprises that have core data that needs to be stored locally and some businesses that can take advantage of the cloud. Each deployment method has its unique advantages and applicable scenarios, and decisions need to be made after comprehensive consideration.
(III) Functional design
Detailed functional planning: Comprehensively and in-depth planning of the core functions that the digital twin system should have. Real-time data monitoring is one of the basic functions. Through sensors and other equipment, various data in the production process, such as temperature, humidity, pressure, etc., are collected in real time and displayed intuitively on the system interface, so that operators can grasp the production status at any time. The virtual simulation function can provide strong support for enterprise decision-making by simulating real scenes. For example, in the field of construction engineering, virtual simulation of the construction process through the digital twin model can detect possible problems in the construction process in advance, optimize the construction plan, and reduce construction risks. The fault warning function uses data analysis algorithms to analyze the collected data in real time, detect potential fault risks in advance, issue alarms in time, and reduce downtime losses.
UI design: Adhere to the design concept of simplicity, intuitiveness, and beauty, and carefully create the user interface. The interface design should conform to the principles of ergonomics, the layout of the operation buttons should be reasonable, and the information display should be clear and concise. Taking the digital twin interface of the industrial automation control system as an example, it adopts a simple icon design and intuitive operation process. Operators can quickly get started without complex training and easily find the required functions, which greatly improves work efficiency and user experience.
Interaction design: Design a reasonable and smooth interaction method so that users can interact with virtual models naturally and efficiently. In some high-end digital twin display systems, through gesture control technology, users can rotate and scale virtual models just like operating real objects; voice command interaction allows users to easily control the system even when their hands are busy. For example, in smart factories, workers can use voice commands to query the operating status of equipment and issue operation tasks.
(IV) Professional software integration
Clearly integrate software: comprehensively sort out various professional software that need to be integrated in system function design. In the field of mechanical manufacturing, in order to build accurate product models, it is necessary to integrate professional modeling software such as SolidWorks and UG; in order to deeply explore the value behind production data, it may be necessary to integrate data analysis software such as Python data analysis library and MATLAB. These professional software each have powerful functions, and through integration, they can complement each other's advantages and improve the overall performance of the digital twin system.
Formulate integration strategies: formulate personalized integration technology implementation strategies for different types of professional software. For the integration of modeling software and digital twin systems, it is necessary to ensure the accurate transmission and format conversion of model data, and it may be necessary to develop a special data interface; for the integration of data analysis software, it is necessary to consider the real-time nature of the data and the compatibility of the analysis algorithm. By establishing a data sharing mechanism, the data can be smoothly circulated between different software systems, thereby achieving collaborative work.
(V) Functional development
Follow the development process: strictly follow the industry standard process of software development, starting from the functional design stage, carefully plan the development of the front-end and back-end. Front-end development focuses on the presentation and interaction effects of the user interface, and uses advanced front-end frameworks such as Vue.js and React to create a beautiful and smooth user interface. Back-end development focuses on business logic processing and data storage management, and uses stable and reliable server-side languages such as Java and Python to ensure reliable code quality and clear structure, laying a solid foundation for the stable operation of the system.
Deployment and testing: After completing the development work, deploy the system to the actual operating environment and carry out comprehensive multi-dimensional testing. Functional testing mainly verifies whether the various functions of the system meet the design requirements, such as whether the data collection is accurate and whether the virtual simulation results are consistent with the actual situation; performance testing evaluates the system's operating performance under high load, including indicators such as system response time and throughput; compatibility testing ensures that the system can run stably on different devices and operating systems, such as PC, mobile, and different versions of Windows and Linux systems.
(VI) System Operation and Maintenance
Establish an operation and maintenance organization: Establish an operation and maintenance team with strong professional qualities. Team members should include system administrators, network engineers, database administrators, etc. Clarify the responsibilities and division of labor of each member in the system operation and maintenance process. The system administrator is responsible for the operation monitoring and configuration management of the overall system; the network engineer ensures the stability and smoothness of the network; and the database administrator focuses on the safe storage and efficient management of data. Ensure that when problems occur in the system, they can be responded to and resolved quickly.
Formulate an operation and maintenance strategy: Formulate a complete operation and maintenance strategy covering key links such as daily monitoring, troubleshooting, and system upgrades. By monitoring the system operation status in real time and using monitoring software to monitor system performance indicators in real time, potential problems can be discovered in a timely manner; a fault handling process is established so that when a fault occurs, the problem can be quickly located and effective solutions can be taken; the system is regularly upgraded and optimized, and system functions are continuously improved based on user feedback and business development needs to ensure that the system can always provide users with high-quality and efficient services.