## 1 Introduction  
With the continuous development of emerging technologies such as cloud computing, the Internet of Things (IoT), and big data, countries around the world are actively promoting the integration of traditional equipment in the physical and digital domains and advancing intelligent operations to achieve intelligent manufacturing [1, 2]. Intelligent manufacturing represents the deep integration of intelligent and traditional technologies, and its core purpose is to enhance the traditional manufacturing process by applying intelligent technologies throughout the product life cycle. However, how to realize the real-time interaction between virtual information and physical equipment information in the process of equipment development is one of the main challenges facing the application of intelligent manufacturing concepts in actual production development [3]. As research into digital twins deepens, the integration of the field into the industry is gradually increasing. When the concept of digital twins permeates the development of traditional devices, a novel and viable approach to intelligent debugging emerges. The main idea of this approach is to use digital twin technology to interact virtual information with physical device information in real time so as to achieve virtual control and optimization of the device. By creating a digital model of the equipment, it is possible to simulate and optimize the control process in a virtual environment, improving operational efficiency and operational quality. At the same time, through real-time monitoring and analysis of the state and operation data of physical equipment, the development strategy can be adjusted in time to realize the intelligence and automation of the development process. Digital twin is an innovative technology that simulates the behavior of physical entities in the real world by creating a virtual model of those entities and using that model and associated data to simulate the behavior of those entities in the real world. This technology injects new functionality into physical entities, enabling interactive feedback between virtual reality and the real world, integrated analysis of data, and continuous optimization of the decision-making process [4]. At all stages of the product lifecycle, digital twin technology uses modeling, data analytics, and interdisciplinary techniques to build a bridge between the physical world and the information world so that it can provide more real-time, efficient, and intelligent services [2; 5; 6]. With the development of the new round of information technology, digital twin has gradually become the driving force of intelligent manufacturing and the bridge and link between the physical world and the information world [4]. The application of digital twins in industrial and life scenarios typically goes through three phases. Firstly, virtual debugging is performed to optimize the model by solving problems in the design. Secondly, real-world signals interact with the virtual world to enable virtual debugging. Finally, applying digital twins to the real world. Virtual debugging helps to identify and resolve problems early, as well as reduce manufacturing errors and improve R&D efficiency. Many scholars have conducted related research, and digital twins were first proposed by [PERSON] for full product lifecycle management [7]. In 2015, [PERSON] et al. proposed to build a digital dual model for modern manufacturing [8]. [PERSON] et al. argue that digital twin technology should not be limited to a particular product and can be applied throughout the development process of modern manufacturing [9]. [PERSON] et al. applied digital twin technology in a logistics smart factory [10]. [PERSON] et al. explored the digital twin factory, implementing methods such as real-time data collection as well as simulation-based data processing [11]. [PERSON] et al.