School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, China.
Sensors (Basel). 2022 Aug 25;22(17):6391. doi: 10.3390/s22176391.
Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value.
由于工业互联网技术的飞速发展，传统制造业迫切需要进行数字化转型，而实现这一目标的关键技术之一就是多源数据融合。针对这一问题，本文提出了一种改进的基于熵权的逼近理想解排序法（TOPSIS）用于多源数据融合评价系统。该方法在决策级融合算法的基础上增加了融合评价系统，并提出了一种动态融合策略。融合评价系统有效地解决了多源数据之间数据规模不一致导致的模型融合困难和融合精度低的问题，得到了最优的融合结果。然后，本文通过对单源数据的多层特征融合和多源数据的决策级融合进行实验，验证了融合评价系统的有效性。本文的研究结果可应用于离散行业的智能生产和装配厂，为其提供相应的管理和决策支持，具有一定的实用价值。
