基于特征融合并行优化模型的环境γ辐射剂量率数据分析与预测  

Prediction of HPIC dose rate in radiation environment based on feature fusion and parallel optimization model

作　　者：刘君武 吴允平 林明贵 LIU Junwu;WU Yunping;LIN Minggui(College of Photonic and Electronic Engineering,Fujian Normal University,Fuzhou 350007;Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring,Fuzhou 350007;Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application,Fujian Normal University,Fuzhou 350007;Fujian Radiation Environment Supervision Station,Fuzhou 350013)

机构地区：[1]福建师范大学光电与信息工程学院,福州350007 [2]数字福建环境监测物联网实验室,福州350007 [3]福建省光电传感应用工程技术研究中心,福州350007 [4]福建省辐射环境监督站,福州350013

出　　处：《辐射防护》2024年第2期126-133,共8页Radiation Protection

基　　金：国家自然科学海峡联合基金重点项目(No.U1805263);福建省自然科学基金项目(No.2019J01427);江西省核地学数据科学与系统工程技术研究中心资助项目(JETRCNGDSS202101)。

摘　　要：核电辐射环境监测网(ERMS)能提供实时、连续的监测数据,是核电最重要的外围监督性设施,为辐射环境评估提供数据依据。为掌握影响辐射数据质量的特征要素与及时发现环境的辐射异常,开展高压电离室探测器(HPIC)剂量率数据的特征挖掘与预测研究,提出一种基于奇异谱分析算法(singular spectrum analysis,SSA)的γ辐射剂量率数据预处理方法,从其历史数据中学习涨幅趋势和拐点细节变化;针对数据的多维度特点,设计一种特征融合并行优化模型预测框架,以福建宁德核电站外围11个自动站辐射监测数据、天顶方向总电子含量(VTEC)数据进行实验验证。实验结果表明,该模型对环境γ辐射剂量率预测取得了较好的预测性能与精度。Environment radiation monitoring system(ERMS)around nuclear power plant can provide real-time and continuous monitoring data,which is the most important peripheral supervision facility of nuclear power plant and provides data basis for radiation environment assessment.In order to master the characteristic elements that affect the quality of radiation data and timely detect environmental radiation anomalies,data feature exploring and prediction research ofγradiation dose rate data were carried out.A preprocessing method of HPIC dose-rate data based on singular spectrum analysis was proposed to learn the increase trend and inflection point details from its historical data.According to the multidimensional characteristics of data,a SSA feature fusion parallel optimization model prediction framework was designed,and simulation experiments were carried out.Data of 11 automatic radiation monitoring stations around Ningde Nuclear Power Plant in Fujian Province and Vertical Total Electron Content(VTEC)were used for experimental verification.The experimental results show that the feature fusion network model achieves good prediction performance and accuracy forγradiation dose rate prediction.

关 键 词：时间序列 辐射环境 高压电离室探测器 Γ辐射剂量率 奇异谱分析 特征融合网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]