辐射防护2024，Vol.44Issue(2)：126-133,8.
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
摘要
Abstract
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·132·exploring and prediction research of γ radiation 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 for γ radiation 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Key words
time series/radiation environment/HPIC/γ radiation dose rate/singular spectrum analysis/feature fusion network分类
信息技术与安全科学引用本文复制引用
刘君武,吴允平,林明贵..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[J].辐射防护,2024,44(2):126-133,8.基金项目
国家自然科学海峡联合基金重点项目(No.U1805263) （No.U1805263）
福建省自然科学基金项目(No.2019J01427) （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). （JETRCNGDSS202101）
