[1] 荣健, 刘展. 先进核能技术发展与展望 [J]. 原子能科学技术, 2020, 54(9): 1638-1643. DOI:  10.7538/yzk.2020.youxian.0348.

RONG J, LIU Z. Development and prospect of advanced nuclear energy technology [J]. Atomic Energy Science and Technology, 2020, 54(9): 1638-1643. DOI:  10.7538/yzk.2020.youxian.0348.
[2] 王海洋, 荣健. 碳达峰、碳中和目标下中国核能发展路径分析 [J]. 中国电力, 2021, 54(6): 86-94. DOI:  10.11930/j.issn.1004-9649.202103141.

WANG H Y, RONG J. Analysis on China's nuclear energy development path under the goal of peaking carbon emissions and achieving carbon neutrality [J]. Electric Power, 2021, 54(6): 86-94. DOI:  10.11930/j.issn.1004-9649.202103141.
[3] 蔡绍宽. 双碳目标的挑战与电力结构调整趋势展望 [J]. 南方能源建设, 2021, 8(3): 8-17. DOI:  10.16516/j.gedi.issn2095-8676.2021.03.002.

CAI S K. Challenges and prospects for the trends of power structure adjustment under the goal of carbon peak and neutrality [J]. Southern Energy Construction, 2021, 8(3): 8-17. DOI:  10.16516/j.gedi.issn2095-8676.2021.03.002.
[4] 王鑫, 吴继承, 朴磊. “双碳”目标下核能发展形势思考 [J]. 核科学与工程, 2022, 42(2): 241-245. DOI:  10.3969/j.issn.0258-0918.2022.02.001.

WANG X, WU J C, PU L. Consideration of the development situation of nuclear power under the goal of carbon peaking and carbon neutraulity [J]. Nuclear Science and Engineering, 2022, 42(2): 241-245. DOI:  10.3969/j.issn.0258-0918.2022.02.001.
[5] 蒋祖跃. 秦山核电厂反应堆保护系统及其相关设备数字化改造规划和实施策略 [J]. 原子能科学技术, 2010, 44(1): 65-69. DOI:  10.7538/yzk.2010.44.01.0065.

JIANG Z Y. Upgrading planning and executive strategy for reactor protection system and relative equipment in Qinshan nuclear power plant [J]. Atomic Energy Science and Technology, 2010, 44(1): 65-69. DOI:  10.7538/yzk.2010.44.01.0065.
[6] 高麒瀚, 江德正. 智能化工艺系统设计平台技术在核电工程设计的应用探讨 [J]. 核科学与工程, 2014, 34(1): 125-133+141. DOI:  10.3969/j.issn.0258-0918.2014.01.020.

GAO Q H, JIANG D Z. Study on application of intelligent process system design platform technology in nuclear engineering [J]. Nuclear Science and Engineering, 2014, 34(1): 125-133+141. DOI:  10.3969/j.issn.0258-0918.2014.01.020.
[7] AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules in large databases [C]// Anon. Proceedings of the 20th international conference on very large data bases, Santiago de Chile, Chile, September 12-15, 1994. Santiago de Chile: Morgan Kaufmann Publishers Inc, 1994: 487-499.
[8] 程路明, 楼平, 诸骏豪, 等. 基于APRIORI-贝叶斯优化XGBoost的电力通信网根告警预测 [J]. 电力建设, 2022, 43(1): 113-121. DOI:  10.12204/j.issn.1000-7229.2022.01.013.

CHENG L M, LOU P, ZHU J H, et al. Root alarm prediction of power communication network applying APRIORI-Bayesian optimization XGBoost [J]. Electric Power Construction, 2022, 43(1): 113-121. DOI:  10.12204/j.issn.1000-7229.2022.01.013.
[9] 廖孟柯, 樊冰, 李忠政, 等. 基于改进Apriori算法的配电网设备退役信息挖掘 [J]. 科学技术与工程, 2021, 21(24): 10381-10386. DOI:  10.3969/j.issn.1671-1815.2021.24.039.

LIAO M K, FAN B, LI Z Z, et al. Mining of distribution network equipment decommissioning factors based on improved Apriori algorithm [J]. Science Technology and Engineering, 2021, 21(24): 10381-10386. DOI:  10.3969/j.issn.1671-1815.2021.24.039.
[10] HAN J W, PEI J, YIN Y W. Mining frequent patterns without candidate generation [J]. ACM SIGMOD Record, 2000, 29(2): 1-12. DOI:  10.1145/335191.335372.
[11] 肖永立, 刘松, 见伟, 等. 一种基于FP-growth算法的变电站二次设备缺陷分析方法 [J]. 电测与仪表, 2020, 57(12): 83-90. DOI:  10.19753/j.issn1001-1390.2020.12.013.

XIAO Y L, LIU S, JIAN W, et al. A kind of defects analysis method for secondary device of substation based on FP-growth algorithm [J]. Electrical Measurement & Instrumentation, 2020, 57(12): 83-90. DOI:  10.19753/j.issn1001-1390.2020.12.013.
[12] 张斌, 滕俊杰, 满毅. 改进的并行fp-growth算法在工业设备故障诊断中的应用研究 [J]. 计算机科学, 2018, 45(增刊1): 508-512.

ZHANG B, TENG J J, MAN Y. Application research of improved parallel fp-growth algorithm in fault diagnosis of industrial equipment [J]. Computer Science, 2018, 45(Supp. 1): 508-512.
[13] 方晓洁, 黄伟琼, 叶东华, 等. 分布式并行FP-growth算法在二次设备缺陷监测中的应用 [J]. 电力系统保护与控制, 2021, 49(8): 160-167. DOI:  10.19783/j.cnki.pspc.200715.

FANG X J, HUANG W Q, YE D H, et al. Application of a distributed parallel FP-growth algorithm in secondary device defects monitoring [J]. Power System Protection and Control, 2021, 49(8): 160-167. DOI:  10.19783/j.cnki.pspc.200715.
[14] 何望, 林果园. 基于FP-Growth改进算法的云服务器故障数据分析 [J]. 计算机工程与科学, 2020, 42(5): 770-775. DOI:  10.3969/j.issn.1007-130X.2020.05.002.

HE W, LIN G Y. Analysis of cloud server fault data based on improved FP-Growth algorithm [J]. Computer Engineering & Science, 2020, 42(5): 770-775. DOI:  10.3969/j.issn.1007-130X.2020.05.002.
[15] 纪德洋, 金锋, 冬雷, 等. 基于皮尔逊相关系数的光伏电站数据修复 [J]. 中国电机工程学报, 2022, 42(4): 1514-1522. DOI:  10.13334/j.0258-8013.pcsee.211172.

JI D Y, JIN F, DONG L, et al. Data repairing of photovoltaic power plant based on Pearson correlation coefficient [J]. Proceedings of the CSEE, 2022, 42(4): 1514-1522. DOI:  10.13334/j.0258-8013.pcsee.211172.
[16] 张婧, 任刚. 城市道路交通拥堵状态时空相关性分析 [J]. 交通运输系统工程与信息, 2015, 15(2): 175-181. DOI:  10.16097/j.cnki.1009-6744.2015.02.027.

ZHANG J, REN G. Spatio-temporal correlation analysis of urban traffic congestion diffusion [J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(2): 175-181. DOI:  10.16097/j.cnki.1009-6744.2015.02.027.
[17] 张华, 龙呈, 胡思洋, 等. 基于层次聚类法与皮尔逊相关系数的配电网拓扑校验方法 [J]. 电力系统保护与控制, 2021, 49(21): 88-96. DOI:  10.19783/j.cnki.pspc.210075.

ZHANG H, LONG C, HU S Y, et al. Topology verification method of a distribution network based on hierarchical clustering and the Pearson correlation coefficient [J]. Power System Protection and Control, 2021, 49(21): 88-96. DOI:  10.19783/j.cnki.pspc.210075.
[18] 刘永阔, 谢春丽, 成守宇, 等. 核电站分布式智能故障诊断系统研究与设计 [J]. 原子能科学技术, 2011, 45(6): 688-694.

LIU Y K, XIE C L, CHENG S Y, et al. Research and design of distributed intelligence fault diagnosis system in nuclear power plant [J]. Atomic Energy Science and Technology, 2011, 45(6): 688-694.
[19] JUSTUSSON B J. Median filtering: statistical properties [M]//HUANG T S. Two-dimensional digital signal prcessing II: transform and median filters. Berlin Heidelberg: Springer, 1981. DOI: 10.1007/BFB0057597.
[20] 叶林, 滕景竹, 蓝海波, 等. 变尺度时间窗口和波动特征提取的短期风电功率组合预测 [J]. 电力系统自动化, 2017, 41(17): 29-36. DOI:  10.7500/AEPS20161201016.

YE L, TENG J Z, LAN H B, et al. Combined prediction for short-term wind power based on variable time window and feature extraction [J]. Automation of Electric Power Systems, 2017, 41(17): 29-36. DOI:  10.7500/AEPS20161201016.