Research on the Application and Sustainable Development of Digital Twin Technology Based on Big Data Technology and Deep Learning in the Green Transformation of Manufacturing Industry

With the increasing global attention to sustainable development, traditional manufacturing is accelerating its transformation towards green, intelligent, and service-oriented directions. To address various challenges in green manufacturing, manufacturing companies are gradually introducing advanced technologies such as digital twins, the Internet of Things (IoT), and Cyber Physical Systems (CPS). This article proposes a comprehensive evaluation method based on digital twins, which combines bill of materials (BOM) and energy consumption bill of materials (ECBOM) data, and achieves energy efficiency evaluation throughout the entire lifecycle of manufacturing services by integrating the energy efficiency characteristics of virtual and physical spaces. During the research process, this article constructed a five dimensional energy efficiency evaluation model and innovatively proposed a multi perspective BOM to ECBOM conversion method. In addition, a real-time feedback mechanism has been designed to ensure effective data transfer between physical and virtual spaces. The research results indicate that this method significantly improves the accuracy of energy efficiency assessment, and BOM and ECBOM play a crucial role as data links in energy efficiency assessment in virtual and physical spaces. The method proposed in this article has important application prospects for promoting the green transformation and sustainable development of the manufacturing industry.