Global positioning of high-value medical equipments based on intelligent tags of Internet of things(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2021年第9期
- Page:
- 1168-1171
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Global positioning of high-value medical equipments based on intelligent tags of Internet of things
- Author(s):
- HUANG Zaiquan1; 3; WU Kai2; LU Guangwen1
- 1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China 2. School of Biomedical Sciences and
Engineering, South China University of Technology, Guangzhou 511442, China 3. the Sixth Affiliated Hospital of Guangzhou Medical
University/Qingyuan Peoples Hospital, Qingyuan 511518, China
- Keywords:
- Internet of things intelligent tag high-value medical equipment global positioning
- PACS:
- R318;TP391
- DOI:
- 10.3969/j.issn.1005-202X.2021.09.022
- Abstract:
- Objective Aiming at the problem of the existing management model of high-value medical equipments deploys
equipments only according to the equipment characteristics of a department, which leads to a low accuracy of model positioning
when it is applied to global deployment, a method for the global positioning of high-value medical equipments based on the
intelligent tags of Internet of things is proposed. Methods Internet of things was combined with RFID. By identifying the tags of
the global positioning of the equipments, the feature codes of the tag positioning data were extracted, and then the parameter feature
codes were used for hybrid networking control.After that the node deployment model of Internet of things for the global positioning
of high-value medical equipments was constructed, the parameters of the intelligent tag deployment model of Internet of things
were clustered and fused. Results and Conclusion The proposed method achieves a high accuracy of global positioning of highvalue
medical equipments, with an average accuracy of 95%, which improves the equipment utilization rate after positioning and
deployment, with an average equipment utilization rate of 90%.
Last Update: 2021-09-27