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:2019,28(11):37-44
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基于Gradient Boosting算法的ERMS辐射数据预测
(1.福建师范大学 光电与信息工程学院, 福州 350007;2.福建省辐射环境监督站, 福州 350013;3.数字福建环境监测物联网实验室, 福州 350117;4.福建省光电传感应用工程技术研究中心, 福州 350000)
Prediction of ERMS Radiation Data Based on Gradient Boosting Algorithm
(1.College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China;2.Fujian Radiation Environment Supervision Station, Fuzhou 350013, China;3.Digital Fujian Environmental Monitoring IoT Laboratory, Fuzhou 350117, China;4.Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fuzhou 350007, China)
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本文已被:浏览 23次   下载 15
投稿时间:2019-04-12    修订日期:2019-05-08
中文摘要: 影响核辐射监测站点辐射监测HPIC剂量率实时数据准确性的组成因素多且复杂,如自然因素的降雨、温湿度、风向及太阳辐射等,客观因素的设备异常及放射性状况等;以致在实际应用中发现辐射监测状态异常时,很难分析出是什么原因导致的监测数据偏离.结合ERMS海量历史辐射序列监测数据,深入挖掘降雨、温湿度、气压、风向、太阳辐射天顶方向电子量及周边各站点辐射数值等特征因子集,基于Gradient Boosting算法(简称GB算法)建立起HPIC剂量率辐射数据的在线预测模型,有效融合自然特征因子,降低了自然因子对HPIC剂量率辐射监测数值异常的分析及判读的干扰作用,提高了对ERMS辐射异常发现的辅助判断能力及维保效率.
中文关键词: HPIC剂量率  异常  ERMS  GB算法  预测
Abstract:The factors affecting the accuracy of real-time data of HPIC dose rate in nuclear radiation monitoring stations are complex, such as natural factors of rainfall, temperature and humidity, wind direction and solar radiation, objective factors of equipment anomalies and radioactivity, etc. When it is found that the radiation monitoring state is abnormal, it is difficult to analyze the cause of the deviation of the monitoring data. Combined with the monitoring data of massive historical radiation series of ERMS, the characteristics of rainfall, temperature and humidity, air pressure, wind direction, electrons in the zenith direction of solar radiation and the radiation values of surrounding sites are deeply explored. HPIC is established based on the Gradient Boosting algorithm (referred to as GB algorithm). The online prediction model of dose rate radiation data effectively combines the natural characteristic factors, reduces the natural factor's analysis of the HPIC dose rate radiation monitoring numerical anomaly and the interference effect of interpretation, and improves the auxiliary judgment ability and maintenance efficiency of ERMS radiation abnormality discovery.
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基金项目:国家自然科学基金海峡联合基金(U1805263);福建省自然科学基金(2019J01427);福建省引导性项目(2019H0009)
引用文本:
朱武峰,王廷银,林明贵,苏伟达,李汪彪,吴允平.基于Gradient Boosting算法的ERMS辐射数据预测.计算机系统应用,2019,28(11):37-44
ZHU Wu-Feng,WANG Ting-Yin,LIN Ming-Gui,SU Wei-Da,LI Wang-Biao,WU Yun-Ping.Prediction of ERMS Radiation Data Based on Gradient Boosting Algorithm.COMPUTER SYSTEMS APPLICATIONS,2019,28(11):37-44

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