[1]李柏杭,王新安,雍珊珊,等.人工免疫算法在 AETA 异常检测中的应用研究[J].计算机技术与发展,2019,29(03):1-5.[doi:10.3969/ j. issn.1673-629X.2019.03.001]
　LI Bo-hang,WANG Xin-an,YONG Shan-shan,et al.Research on Application of Artificial Immune Algorithm in Discriminating Abnormal Data of AETA Equipment[J].,2019,29(03):1-5.[doi:10.3969/ j. issn.1673-629X.2019.03.001]
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人工免疫算法在 AETA 异常检测中的应用研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年03期
页码:
1-5
栏目:
智能、算法、系统工程
出版日期:
2019-03-10

文章信息/Info

Title:
Research on Application of Artificial Immune Algorithm in Discriminating Abnormal Data of AETA Equipment
文章编号:
1673-629X(2019)03-0001-05
作者:
李柏杭; 王新安; 雍珊珊; 徐伯星; 黄继攀
北京大学 深圳地震监测预测技术研究中心,广东 深圳 518055
Author(s):
LI Bo-hang; WANG Xin-an; YONG Shan-shan; XU Bo-xing; HUANG Ji-pan
Shenzhen Earthquake Monitoring and Prediction Technology Research Center,Peking University, Shenzhen 518055,China
关键词:
人工免疫算法; 异常检测; 地震数据分析; 数据挖掘
Keywords:
AIS; abnormal detection; earthquake data analysis; data mining
分类号:
TP39
DOI:
10.3969/ j. issn.1673-629X.2019.03.001
摘要:
AETA 多分量地震监测系统已经在中国四川、云南、河北、广东、西藏、台湾等地区布设 200 余套。 一套 AETA 设备每个月产生 7 G 数据,查看 200 套设备的数据需要花费大量的时间和精力,而且人工判断异常容易漏检。 利用人工免疫算法中负向选择的思想,通过对正常数据的学习获得相应的抗体库,根据待测数据与抗体库中抗体的匹配程度对设备数据进行异常检测。 另外,结合 AETA 设备数据特点,改进了人工免疫算法中的亲和力函数,减少了计算量。 首先,从 AETA 设备中选取了 8 个典型台站的低频电磁数据的正常数据进行训练,得到抗体库,然后对待测数据进行异常检测。 利用该方法,可以有效检测出系统中常见的 3 种异常数据,3 种异常的检测成功率分别为 87. 20%、83. 33%、55. 56%。 可以实现异常数据的初步判定。
Abstract:
AETA multi-component seismic monitoring system has been deployed in more than 200 sets in Sichuan,Yunnan,Hebei, Guangdong,Tibet and Taiwan. A set of AETA equipment generates 7G of data every month,and it takes a lot of time and effort to view the data of 200 sets of equipment,and it is easy to miss detection by manual judgment. Using the idea of negative selection in the artificial immune algorithm,the corresponding antibody library is obtained by learning the normal data. According to the matching degree between the data to be tested and the antibodies in the antibody library,the abnormal detection of equipment data is carried out. In addition, combined with the data characteristics of AETA equipment,the affinity function in the artificial immune algorithm is improved to reduce the amount of calculation. First,the normal data of low-frequency electromagnetic data of 8 typical stations are selected from the AETA equipment for training to obtain the antibody library,and then anomaly detection is performed on the measured data. By this method,it is possible to effectively detect three types of abnormal data in the system. The detection accuracy of the three abnormalities are 87.20%, 83. 33% and 55. 56% respectively. A preliminary determination of abnormal data can be achieved.

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更新日期/Last Update: 2019-03-10