The value and significance of digital twin applications
(1) The value of monitoring and control aggregation - improving decision-making efficiency
In the past, enterprise data analysis and decision-making were often limited to a single perspective, and only some key data could be obtained, making it difficult to form a comprehensive insight. In terms of monitoring and control, they mostly relied on local monitoring and manual control by on-site personnel. This model is not only inefficient, but also easily interfered by human factors. Today, the emergence of digital twin technology has brought about a revolutionary breakthrough. By deploying a large number of sensors distributed in different locations and at different levels, all kinds of data of physical entities during operation are collected in all directions, covering multiple dimensions such as temperature, pressure, vibration frequency, and operating speed. At the same time, using advanced Internet of Things technology, these data are transmitted to the central control system in real time to build a multi-dimensional and comprehensive data analysis system. On this basis, big data analysis algorithms and intelligent models are used to achieve remote online aggregation monitoring. For example, in smart grids, digital twin technology can be used to monitor the power transmission and equipment operation status of each node in the grid in real time. Once an abnormality is found, the system can quickly perform intelligent diagnosis, accurately locate the fault point, and implement precise control, such as automatically adjusting power distribution and switching backup lines. This transformation has greatly broadened the breadth and depth of data acquisition, providing a richer and more accurate information basis for decision-making, thereby significantly improving decision-making efficiency.
(2) The value of simulation and early warning prediction - improving equipment life
The traditional equipment maintenance mode is mostly post-maintenance or regular maintenance. The former is prone to production interruption and huge losses; the latter may increase unnecessary costs due to excessive maintenance. Digital twin technology innovatively integrates three-dimensional models with mechanism models to build a highly realistic equipment virtual model. This model not only has an appearance and structure that is completely consistent with the real equipment, but also can accurately simulate the physical process and operation mechanism inside the equipment. At the same time, it is closely combined with real-time operation data monitoring, using sensors to collect equipment operation parameters in real time and feed them back to the virtual model. When the data in the virtual model fluctuates abnormally, the system will immediately trigger the early warning mechanism, and can accurately detect hidden dangers before potential accidents occur. For example, in the maintenance of aircraft engines, the digital twin system can predict the wear of engine components based on the real-time operation data of the engine, such as fuel flow, blade speed, combustion chamber temperature, etc., and arrange maintenance personnel to perform predictive maintenance in advance. This not only effectively reduces the probability of equipment failure, but also significantly extends the service life of the equipment, reduces the cost of equipment replacement for enterprises, and ensures production continuity.
(3) The value of multi-person collaborative visual training - strengthening training effects, reducing learning costs, and promoting teamwork
Previous personnel training methods often rely on boring theoretical explanations and limited on-site demonstrations, which have poor training effects and low employee learning enthusiasm. Digital twin technology adopts an innovative training model with game levels to bring employees a new learning experience. With the help of high-definition graphics rendering technology, virtual reality (VR) and augmented reality (AR) technology, complex equipment structures, process flows, etc. are clearly presented with high-definition and intuitive images. For example, in the training of petrochemical companies, through the digital twin system, employees can observe the internal structure of large-scale refining equipment and the flow process of crude oil in the pipeline in an immersive way. At the same time, the highly interactive operation experience allows employees to personally operate virtual equipment to perform troubleshooting, maintenance drills, and other operations. Whether it is the key display of pipelines and lines, or the vivid presentation of equipment disassembly animations, details that are difficult to directly observe in real scenes are intuitively presented. In the multi-person collaboration mode, employees from different departments and positions can participate in training at the same time and complete specific tasks together. For example, in the training of power system fault repair, operation and maintenance personnel, technicians, and managers can work together in a virtual environment to simulate the actual repair process, thereby enhancing the communication and collaboration capabilities among team members. This not only improves the individual training effect, but also reduces the overall learning cost through the multi-person collaboration mode.
(4) The value of production process visualization management - optimizing the automation process
In the traditional production process, due to the lack of intuitive visualization of the production process, there are many obstacles when the human production factor is integrated into the production automation process, resulting in low production efficiency and low collaboration quality. Digital twin technology gives the automated production process an intuitive visualization effect. By establishing a digital twin model of the production process, the equipment operation status, material flow, and personnel operation process on the production line are displayed in real time on a unified platform. For example, in automobile manufacturing companies, through the digital twin system, managers can see the assembly process of automobile parts on the production line and the operation of robots in real time. This enables the human production factor to be more efficiently integrated into the process combing and optimization of production automation. In this process, people and machines, equipment and materials can collaborate quickly and efficiently. For example, when a bottleneck is found in a certain production link, the personnel configuration and equipment operation parameters can be quickly adjusted through the analysis of the digital twin system, which greatly improves the production efficiency and coordination quality, and promotes the entire production process to a new stage of greater intelligence and efficiency.
Application significance of digital twin technology
Since its conception, digital twin technology has developed rapidly and has played a huge role in promoting product design, manufacturing and services.
(1) More convenient and more conducive to innovation
Digital twins use digital means such as design tools, simulation software, the Internet of Things and virtual reality to accurately map the properties of physical devices to virtual space and construct a flexible digital mirror. For example, in the design of electronic products, designers can use digital twin technology to repeatedly design and modify the appearance and internal structure of the product in the virtual space without making a large number of physical prototypes. This mirror has the characteristics of disassembly, copying, transfer, modification, deletion and repeated operation, which greatly accelerates the operator's cognitive process of physical entities. Many operations that were previously difficult to complete due to physical constraints, such as simulation, batch replication, and virtual assembly, are now within reach. This not only lowers the threshold for operation, but also inspires people to explore new ways and injects a steady stream of innovative vitality into optimizing design, manufacturing, and service processes. For example, in the aerospace field, through digital twin technology, engineers can simulate and test aircraft under various extreme conditions in a virtual environment, thereby optimizing design solutions and improving product performance.
(2) More comprehensive measurement
In the industrial field, accurate measurement is the key to achieving continuous improvement. Whether it is product design, manufacturing, or post-service, it is necessary to accurately grasp the properties, parameters, and operating status of physical entities to support accurate analysis and optimization. Traditional measurement methods rely on expensive physical measurement tools, such as sensors, acquisition systems, and detection systems, which not only limits the measurement coverage, but also makes it helpless for many indicators that cannot be directly collected. Digital twin technology relies on the Internet of Things and big data technology. By collecting limited direct data from physical sensors, relying on a large sample library, and using machine learning algorithms, it can infer indicators that were originally difficult to measure. For example, by using historical data such as lubricating oil temperature, winding temperature, and rotor torque, a fault feature model is constructed through machine learning, thereby indirectly inferring the health indicators of the generator system and providing more comprehensive data support for equipment maintenance. In the manufacturing industry, digital twin technology can be used to predict product fatigue life, reliability and other indicators based on measured product dimensions, surface roughness and other data combined with historical production data, providing a strong basis for product quality control.
(3) More powerful analysis and prediction capabilities
In the existing product life cycle management, accurate prediction has always been a difficult point, making it difficult to gain insight into potential problems in advance. Digital twin technology integrates the data collection of the Internet of Things, the processing of big data and the modeling and analysis capabilities of artificial intelligence. It can not only accurately evaluate the current status and conduct in-depth diagnosis of past problems, but also make scientific predictions about future trends. For example, in the construction of smart cities, digital twin systems can predict future urban development trends, such as traffic congestion and changes in energy demand, based on the city's population flow data, traffic flow data, energy consumption data, etc. Based on the analysis results, digital twins can simulate multiple possibilities and provide decision makers with comprehensive and reliable decision support, helping companies plan ahead and avoid risks. In supply chain management, digital twin technology can predict the risk of supply chain disruption based on market demand changes, raw material supply, logistics and transportation status, and adjust procurement plans and optimize logistics routes in advance to ensure the stable operation of the supply chain.
(4) Realize the digitization of experience
In traditional industrial design, manufacturing and service fields, experience is often difficult to quantify and effectively utilize, and it is difficult to serve as a reliable basis for accurate decision-making. The major breakthrough of digital twin technology is that it can save, copy, modify and transfer expert experience through digital means. Taking large-scale equipment fault diagnosis as an example, through machine learning of sensor historical data, digital feature models for different fault phenomena are trained, and combined with expert processing records, a basis for accurately judging the fault status of the equipment is formed. For example, in the fault diagnosis of high-speed trains, the digital twin system can quickly and accurately determine the type and location of the fault based on data such as vibration, sound, and electrical parameters during the operation of the train, combined with the expert's experience in handling previous faults. At the same time, for new fault forms, the feature library can be continuously enriched and updated to gradually realize the automation of intelligent diagnosis and decision-making. In industrial design, digital twin technology can be used to digitally record and analyze designers’ creative inspirations, design ideas, etc., providing reference and reference for subsequent design projects.