Article: [Analysis of early diagnosis and prevention techniques for occupational pneumoconiosis based on patent perspectives in China's coal industry].
2024 Volume 41, Issue 12, Page(s) 948–955
Abstract: Occupational pneumoconiosis in the coal industry is an occupational disease that seriously endangers coal miners' health. Early diagnosis and prevention techniques are significant in controlling its incidence rate and reducing its harm. This article ... ...
Abstract | Occupational pneumoconiosis in the coal industry is an occupational disease that seriously endangers coal miners' health. Early diagnosis and prevention techniques are significant in controlling its incidence rate and reducing its harm. This article takes the patent data related to the early diagnosis and prevention of occupational pneumoconiosis in the coal industry, published from 1985 to 2021, as the research object. It uses tools such as the Derwent Data Analyzer (DDA) software, the Patyee Database, and the PatSnap Database to analyze the technological development trend and characteristics in this field from aspects of growth trend, primary patent holders, IPC classification layout, value, and research and development hotspots. The results show that the number of patent applications in this field indicates a rapid growth trend, mainly in the hands of Shandong Energy Group Co. Ltd., Shandong University of Science and Technology, China University of Mining and Technology, and other institutions or enterprises. Patent technology mainly involves spray dust reduction, dust removal fans, dust masks, and other aspects with high patent value and innovation ability. This article provides a new perspective and reference for preventing, diagnosing, and treating occupational pneumoconiosis in the coal industry. |
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MeSH term(s) | Humans ; Coal Industry ; Pneumoconiosis/diagnosis ; Pneumoconiosis/prevention & control ; Early Diagnosis ; Dust ; China |
Chemical Substances | Dust |
Language | Chinese |
Publishing date | 2024-01-09 |
Publishing country | China |
Document type | English Abstract ; Journal Article |
ZDB-ID | 1050900-8 |
ISSN | 1001-9391 |
ISSN | 1001-9391 |
DOI | 10.3760/cma.j.cn121094-20230106-00008 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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