一种基于多阈值模板的快速分类在线检测方法

doi:10.16180/j.cnki.issn1007-7820.2024.06.010

Abstract

Abstract:

The traditional off-line data analysis method has many shortcomings in processing the data with high immediacy and large flow, while the online detection model can meet the real-time requirements of data flow analysis. This study proposes an online detection method based on the multi-threshold template. The proposed method combines TSTKS(Ternary Search Tree and Kolmogorov-Smirnov) algorithm for online detection, and updates the window length based on the mutation point density to improve the mutation point detection accuracy. Self-learning, matching and classification of time series data are realized by equal grading strategy, so as to detect and predict the status of large-scale lesion data. The experimental results of simulation experiment and lesion data show that the proposed method has the advantages of high efficiency and accurate classification, which provides a new method for the rapid classification of large-scale time series data.

Key words: time series data, TSTKS algorithm, sliding window, online detection theory, buffer, mutation point density, multi-threshold template, equal grading police

CLC Number:

TP311

XUE Yuxin, QI Jinpeng, JIA Can, YUAN Ao, HUANG Lina. A Fast Classification Online Detection Method Based on Multi-Threshold Template[J].Electronic Science and Technology, 2024, 37(6): 77-83.

Figures/Tables 8

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Table 1.

Table 2.

Figure 5.

Figure 6.

References 20

[1]	刘晓艳, 王珏, 姚铁锤, 等. 基于时序数据处理的分布式光伏功率预测系统[J]. 数据与计算发展前沿, 2021, 3(4):140-148.
	Liu Xiaoyan, Wang Jue, Yao Tiechui, et al. A distributed photovoltaic power prediction system based on time series data processing[J]. Frontiers of Data and Computing, 2021, 3(4):140-148.
[2]	张长青, 杨楠. 基于车联网大数据分析的实时路况检测系统[J]. 电子科技, 2019, 32(8):66-69,74.
	Zhang Changqing, Yang Nan. Design of real-time road condition detection system based on big data analysisof vehicle network[J]. Electronic Science and Technology, 2019, 32(8):66-69,74.
[3]	艾辣椒. 基于多路搜索的时序突变异常快速分析[D]. 上海: 东华大学,2018:66-69.
	Ai Lajiao. A fast analysis for abrupt change detection on time series based on multipath search[D]. Shanghai: Donghua University,2018:66-69.
[4]	胡宇鹏. 时间序列数据挖掘中的特征表示与分类方法的研究[D]. 济南: 山东大学,2018:83-88.
	Hu Yupeng. Research on feature representation and classification methods in time series data mining[D]. Jinan: Shandong University,2018:83-88.
[5]	Casson A, Yates D, Smith S, et al. Wearable electroence-phalography[J]. Engineering in Medicine and Biology Magazine IEEE, 2010, 29(3):44-56.
[6]	Aljawarneh S, Anguera A, Atwood J W, et al. Particulari-ties of data mining in medicine:Lessons learned from patient medical time series data analysis[J]. Journal on Wireless Communications and Networking, 2019(1):1-29.
[7]	朱俊俊. 基于自适应随机策略的多突变点在线检测与病变数据关联分析[D]. 上海: 东华大学,2022:90-94.
	Zhu Junjun. Online detection of multiple change points and association analysis of pathological data based on adaptive random strategy[D]. Shanghai: Donghua University,2022:90-94.
[8]	邹俊晨. 一种基于自适应滑动窗口的突变点在线检测方法[D]. 上海: 东华大学,2019:69-73.
	Zou Junchen. An online change point detection method based on adaptive sliding window[D]. Shanghai: Donghua University,2019:69-73.
[9]	孔凡书, 齐金鹏, 龚汉鑫, 等. 一种基于波动向量分级技术的病变数据快速分析方法[J]. 电子科技, 2022, 35(7):1-6.
	Kong Fanshu, Qi Jinpeng, Gong Hanxin, et al. A fast analysis method of pathological data based on wave vector grading technology[J]. Electronic Science and Technology, 2022, 35(7):1-6.
[10]	龚汉鑫, 齐金鹏, 孔凡书, 等. 一种基于模板匹配与隶属度解析的时序异常快速检测方法[J]. 电子科技, 2022, 35(6):1-5,27.
	Gong Hanxin, Qi Jinpeng, Kong Fanshu, et al. A fast time series anomaly detection method based on template matching and membership analysis[J]. Electronic Science and Technology, 2022, 35(6):1-5,27.
[11]	孔凡书. 基于多突变点检测的波动向量分级技术研究与应用[D]. 上海: 东华大学,2021:53-60.
	Kong Fanshu. Research and application of a wave vector classification technique based on multiple change points detection[D]. Shanghai: Donghua University,2021:53-60.
[12]	Boukerche A, Zhang L, Alfandi O. Outlier detection:Methods,models,and classification[J]. ACM Computing Surveys, 2020, 53(3):1-37.
[13]	Bolgov M V. Bayesian estimation of the change point in the sequences of correlated random variables of hydrometeorological characteristic[J]. Russian Meteorology and Hydrology, 2021, 46(10):667-673.
[14]	展鹏. 基于时间序列挖掘的异常检测关键技术研究[D]. 济南: 山东大学,2020:88-94.
	Zhan Peng. Research on key technologies of anomaly detection based on time series mining[D]. Jinan: Shandong University,2020:88-94.
[15]	张友浩, 赵鸣, 徐梦瑶. 时序数据挖掘的预处理研究综述[J]. 智能计算机与应用, 2021, 11(1):74-78.
	Zhang Youhao, Zhao Ming, Xu Mengyao, et al. Summary of research on preprocessing of time series data mining[J]. Intelligent Computer and Applications, 2021, 11(1):74-78.
[16]	Qi J P, Zhang Q, Qi J, et al. Multi-channel detection for abrupt change based on the ternary search tree and kolmogorov statistic method[C]. Hangzhou: The Thirty- fourth Chinese Control Conference,2015:4968-4973.
[17]	Pearson R K. Outliers in process modeling and identification[J]. IEEE Transactions on Control Systems Technology, 2002, 10(1):55-63.
[18]	邬贺铨. 大数据时代的发展趋势[J]. 广东科技, 2016, 25(17):30-33.
	Wu Hequan. Development trend in the era of big data[J]. Guangdong Science and Technology, 2016, 25(17):30-33.
[19]	Alarcon-aquino V, Barria J A. Anomaly detection in communication networks using wavelets[J]. IEEE Proceedings Communications, 2002, 148(6):55-62.
[20]	贾凡, 严妍, 张家琪. 基于K-means聚类特征消减的网络异常检测[J]. 清华大学学报(自然科学版), 2018, 58(2):137-142.
	Jia Fan, Yan Yan, Zhang Jiaqi. K-means based feature reduction for network anomaly detection[J]. Journal of Tsinghua University(Science and Technology), 2018, 58(2):137-142.

策略	时耗/s	命中率/%	平均相对误差率/%	平均绝对误差率/%
a	0.218 4	0.673 3	0.070 1	0.104 1
b	0.121 0	0.980 0	0.105 6	0.107 8
c	0.131 6	0.995 0	0.101 2	0.101 7
d	0.196 5	0.998 3	0.050 8	0.050 9

初始缓冲区长度	初始滑动窗口长度	时耗/s	命中率/%	平均相对误差率/%	平均绝对误差率/%
2¹¹	2¹³	0.196 5	0.998 3	0.050 8	0.050 9
2¹²	2¹³	0.141 5	0.656 7	0.045 0	0.068 6
2¹¹	2¹⁴	0.162 7	0.915 0	0.066 8	0.073 0
2¹²	2¹⁴	0.131 7	0.643 3	0.035 1	0.054 6

A Fast Classification Online Detection Method Based on Multi-Threshold Template

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 20

Related Articles 15

Metrics

Comments

Recommended 0

[1]	JIA Can, QI Jinpeng, YUAN Ao, XUE Yuxin, DAI Li. An Online Condition Monitoring Method of Electrical Equipment Based on Multiple Change Points and Template Matching [J]. Electronic Science and Technology, 2024, 37(6): 69-76.
[2]	YUAN Ao, QI Jinpeng, JIA Can, XUE Yuxin, GUO Yangyang. A Classification Method of Time Series Pathological Data Segments Based on Deep Learning [J]. Electronic Science and Technology, 2024, 37(6): 84-91.
[3]	KONG Fanshu,QI Jinpeng,GONG Hanxin,ZHU Junjun,CAO Yitong. A Fast Analysis Method of Pathological Data Based on Wave Vector Grading Technology [J]. Electronic Science and Technology, 2022, 35(7): 1-6.
[4]	GONG Hanxin,QI Jinpeng,KONG Fanshu,ZHU Junjun,CAO Yitong. A Fast Time Series Anomaly Detection Method Based on Template Matching and Membership Analysis [J]. Electronic Science and Technology, 2022, 35(6): 1-5.
[5]	ZHU Junjun,QI Jinpeng,ZHONG Jinmei,REN Qing,CAO Yitong. An Online-Method of Multiple Change Points Detection Based on Random and Overlapping Strategy [J]. Electronic Science and Technology, 2022, 35(12): 1-9.
[6]	ZOU Junchen,QI Jinpeng,LI Na,LIU Jialun,ZHU Houjie. Design and Implementation of a Fast Online Algorithm for Mutation Point Detection [J]. Electronic Science and Technology, 2020, 33(8): 10-16.
[7]	HOU Yanming,LI Feifei,CHEN Qiu. Video Retrieval Algorithm Based on Multiple Feature Fusion [J]. Electronic Science and Technology, 2019, 32(5): 44-49.
[8]	NA Yan, DI Peng. Design and Realization of the Audio and Video Signal Acquisition Interface Circuit [J]. , 2016, 29(9): 148-.
[9]	CHU Tian,PAN Jinman,DU Lei. RFID Missing Data Cleaning Algorithm Based on Adaptive Sliding window [J]. , 2016, 29(4): 24-.
[10]	LI Zhenzhu,WANG Xiaohua,ZHOU Yong. Achievement of Frequency Tracking in Power Systems by RPLS Algorithm [J]. , 2015, 28(9): 54-.
[11]	FENG Wanpeng,WANG Ou,JI Yingjun,SONG Zhipeng. A Comprehensive Study of the Encounter-based Low-power Clock Tree [J]. , 2015, 28(3): 132-.
[12]	JIANG Nan,XU Qin. Feature-based Data Stream Clustering Algorithm Projection and Fitting in Data Processing [J]. , 2015, 28(1): 155-.
[13]	QI Xiu-Zhen, ZHOU Ying. Logic Design and Realization of Real-Time Image Acquisition System Based on FPGA [J]. , 2013, 26(4): 22-.
[14]	HONG Jin-Dong, JIAO Feng-Xian, WEI Rong. Design and Implementation of NAT Routing Based on WinPcap [J]. , 2013, 26(12): 53-.
[15]	YUAN Ru-Xuan, WEI Le, HE Tian-Lu, GAO Zhong-Shan. Design of the Double-channel Audio Signal Real-time Acquisition and Processing System Based on DSP [J]. , 2011, 24(7): 24-.