Design and implementation of a fluorescent target historical image data storage system
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摘要: 针对兰州重离子研究装置(HHIRFL)荧光靶历史图像数据存储系统所产生的数据不断递增和历史数据检索速度慢的问题,构建了基于MongoDB数据库的荧光靶历史图像数据存储系统。为了能够保存和观测分析荧光靶束流图像,搭建了基于EPICS的历史数据归档系统获取荧光靶图像过程变量(PV)数据,用MongoDB数据库分片技术对所得数据进行存储,通过Django框架完成图像的转化和Web页面的实现,并在系统中应用了图像分类算法,提高了数据读写的速率。该系统在HIRFL上可以稳定获取、存储、观测荧光靶束流历史图像,为束流分析、调束工作提供了便利。
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关键词:
- 重离子加速器 /
- 荧光靶图像 /
- 数据归档 /
- MongoDB数据库 /
- Django框架
Abstract: A fluorescence target historical image data storage system based on MongoDB database was constructed to address the issues of historical image data storage, continuously increasing data generated by the system, and slow historical data retrieval speed of the Heavy Ion Research Facility in Lanzhou (HIRFL) fluorescence target. In order to save, observe and analyze fluorescence target beam images, this article establishes an EPICS based historical data archiving system to obtain PV (Process Variable) data of fluorescence target images. The obtained data is stored using MongoDB database sharding technology, and the image conversion and web page implementation are achieved through the Django framework. Image classification algorithms are applied in the system to improve data read and write speed. This system can stably obtain, store, and observe fluorescence target beam history images on HIRFL, providing convenience for beam analysis and tuning work.-
Key words:
- heavy ion accelerator /
- fluorescent target image /
- data archiving /
- MongoDB database /
- Django framework
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