Biological analogy analysis of digital economy on the transformation and upgrading of manufacturing industry: Research based on D-S model

This study aims to explore the dynamic relationship between biomechanical modeling and manufacturing productivity in the context of the digital economy, with a particular focus on the interaction between worker movement patterns and production efficiency. By combining biomechanical analysis with VAR (Vector Autoregressive) models, this study reveals the dynamic effects of worker movement patterns on productivity. The VAR model, as a multivariate time series analysis tool, can effectively examine the causal relationship between factors such as production efficiency, labor input, technological level and worker movement patterns. In the study, kinematic and dynamic analysis were combined, with a focus on analyzing biomechanical variables such as the movement trajectory, velocity, acceleration, and applied forces of workers’ arms and joints during the execution of production tasks, in order to evaluate the impact of these factors on production efficiency. Biomechanics modeling helps quantify the effects of long-term repetitive movements on joint load and muscle fatigue in workers, providing potential pathways for optimizing worker movement patterns and work postures to reduce energy consumption and improve productivity. In addition, this study also ranked the development level of the digital economy in various provinces of China through principal component analysis (PCA) and found that the high development of the digital economy is closely related to the construction of information infrastructure. Based on the lag analysis of the VAR model, this study further explores the feedback effects of technological progress and automation level on workers’ movement patterns and production efficiency. The results indicate that with the advancement of automation technology, the interaction mode between workers and automation equipment has changed, and the optimization of worker actions is closely related to productivity improvement. Through this multidimensional analytical framework, this study provides theoretical support for the combination of the digital economy and biomechanics and offers new perspectives and methods for industrial optimization and labor productivity improvement in the manufacturing industry.