基于大数据的个人核辐射剂量预测研究与应用  

Research and Application of Personal Nuclear Radiation Dose Prediction Based on Big Data

作　　者：杨笑千 严静[1] 董传江[1] 郑炯[1] 崔宸 张力丹 YANG Xiaoqian;YAN Jing;ZHENG Jiong;DONG Chuanjiang;CUI Chen;ZHANG Lidan(Nuclear Power Institute of China,Chengdu 610213,China)

机构地区：[1]中国核动力研究设计院,成都610213

出　　处：《自动化与仪器仪表》2024年第9期111-114,121,共5页Automation & Instrumentation

基　　金：四川省科技服务业示范项目(2020GFW047)。

摘　　要：个人受到的核辐射剂量大小不仅取决于环境中的辐射源,还取决于个人的工作方式等多种因素。因此,为了准确预测个人处在核辐射环境下所受剂量的大小,基于大数据技术,通过对多年来采集到的核辐射监测数据进行分析和建模,提出了一种基于大数据的个人核辐射剂量预测方法。结合场所辐射等级、作业时长、是否穿戴防护服、距辐射源距离、科研生产任务等监测数据利用相关性分析方法和机器学习算法,建立了个人核辐射剂量预测模型,并对其进行验证和优化。将该模型应用在核辐射环境下的个人辐射剂量预测和风险评估中,为核辐射防护提供更加有效的手段。本研究表明,基于大数据的个人核辐射剂量预测具有较高的准确性。The amount of nuclear radiation an individual receives not only depends on the radiation source in the environment,but also on various factors such as their work style.Therefore,in order to accurately predict the magnitude of personal nuclear radiation dose in a nuclear radiation environment,this article proposes a personal nuclear radiation dose prediction method based on big data technology by analyzing and modeling the nuclear radiation monitoring data collected over the years.A personal nuclear radiation dose prediction model was established using correlation analysis methods and machine learning algorithms based on monitoring data such as site radiation level,work duration,wearing protective clothing,distance from radiation sources,and scientific research and production tasks,and validated and optimized.Applying this model to personal radiation dose prediction and risk assessment in nuclear radiation environments provides a more effective means for nuclear radiation protection.This study indicates that personal nuclear radiation dose prediction based on big data has high accuracy.

关 键 词：大数据 个人辐射剂量 机器学习算法 数据挖掘 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]