Data fusion-based sustainable digital twin system of intelligent detection robotics

In order to give full play to the role of science and technology in achieving the sustainable development goals, digital twin technology based on data fusion plays an irreplaceable role. This paper analyzes the detection process of intelligent detection robots for massage chairs, theoretical research is carried out from two aspects of decision-level fusion and data-level fusion. The principles, levels, and models of information fusion are analyzed, and an in-depth analysis and discussion of DS evidence theory are conducted. Whereas the ambiguity and uncertainty of evaluation indicators in sustainable design, a quantitative model for sustainable design is established in this paper. The entropy weight method is used to determine the weight of sub-indicators, and a decision matrix for the indicators to be evaluated is established. Based on the data detected by the robot while the massage chair is working, the sustainability of the massage chair is evaluated. Reflect the obtained quantitative indicators on the digital twin data flow system, improve the comprehensive performance of products in sustainable design through data fusion, and prove that the necessity of sustainability and the proposed method must be considered in the design and use process feasibility. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper analyzes the role of digital twin technology in achieving sustainable development goals, focusing on theoretical research on the detection process of massage chairs by intelligent detection robots, information fusion, and DS evidence theory. By establishing a quantitative model, determining weights using the entropy weight method, and evaluating the sustainability of products through data fusion, the comprehensive performance in sustainable design is improved.