环境监测中异常数据识别与修复

景永志; 艾自东; 田相臣

doi:10.12153/j.issn.1674-991X.20230717

环境监测中异常数据识别与修复

doi: 10.12153/j.issn.1674-991X.20230717

青岛科技大学自动化与电子工程学院

基金项目: 青岛市未来产业培育专项(22-3-4-xxgg-11-gx) ; 国家自然科学基金项目(61903213)

详细信息

作者简介:
景永志（1999—），男，硕士研究生，主要研究方向为环境工程、数据异常识别与修复等，qust_wy_003@163.com

通讯作者:
艾自东（1988—），男，副教授，博士，主要研究方向为切换系统控制与优化、事件触发控制、脉冲控制等，aizidong@amss.ac.cn

中图分类号: X831
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- 被引次数: 0
出版历程
- 收稿日期: 2023-10-08
- 录用日期: 2024-01-31
- 修回日期: 2023-12-17

Identification and recovery of abnormal data in environmental monitoring

College of Automation and Electronic Engineering, Qingdao University of Science and Technology

摘要

摘要:
为获取完整、可靠的环境监测数据，提出一种基于GeoHash算法的局部离群因子算法(GeoHash-LOF)。相较于传统的局部离群因子算法(LOF)，GeoHash-LOF算法引入了地址划分和区域编码的思想，降低了算法计算量。针对识别出来的异常数据，采用基于遗传算法改进的灰色预测(GA-GM)算法进行修复，通过对灰色预测中的背景值和初值进行择优，从而提高预测值的准确度。以欧洲核能机构所提供的数据为例，将本文所提出的GeoHash-LOF算法、GA-GM算法与其他算法进行比较，结果表明本文所提出的算法异常数据识别效率更高且缺失数据修复拟合度更好。
- 环境监测 /
- 数据修复 /
- GeoHash-LOF算法 /
- GA-GM算法
Abstract:
A local outlier factor algorithm based on GeoHash approach (GeoHash-LOF) was proposed to obtain comprehensive and reliable environmental monitoring data. Compared to the traditional LOF algorithm, GeoHash-LOF introduced the concepts of address partitioning and region encoding, significantly reducing computational overhead. Identified outlier data was repaired using Genetic Algorithm-improved Grey Model (GA-GM) prediction technique. By optimizing the background value and initial value in the grey prediction model, the accuracy of prediction was enhanced. Taking the data provided by European Nuclear Energy Agency (ENEA) as an example, the proposed GeoHash-LOF algorithm and GA-GM technique were compared with other algorithms. The results demonstrated that the proposed algorithms exhibited higher efficiency in identifying anomaly data and achieved better fit in missing data restoration.
- environmental monitoring /
- data recovery /
- GeoHash-LOF algorithm /
- GA-GM algorithm

HTML全文

图 1 GeoHash编码方式

Figure 1. The encoding of GeoHash

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图 2 区域划分示意

Figure 2. Zoning map

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图 3 第K距离领域

Figure 3. The field of K distance

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图 4 可达距离判定

Figure 4. Determination of reachable distance

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图 5 理想情况区域划分示意

Figure 5. Diagram of ideal area division

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图 6 背景值误差

Figure 6. Background value error

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图 7 拟合曲线

Figure 7. Fitting curve

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图 8 不同K下算法的耗时比较

Figure 8. Comparison of algorithm time consumption under different K

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图 9 GeoHash-LOF识别结果

注：×表示异常数据。

Figure 9. Test results of GeoHash-LOF

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表 1 不同编码长度下单元格平均数据量

Table 1. Average data volume of cells with different coding lengths

编码长度	字节/bits	区域数量	单位区域内平均数据量
1	5	$ {2^5} $	N/$ {2^5} $
2	10	$ {2^{10}} $	N/$ {2^{10}} $
3	15	$ {2^{15}} $	N/$ {2^{15}} $
4	20	$ {2^{20}} $	N/$ {2^{20}} $

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表 2 原始数据

Table 2. Raw data

序号	1	2	3	4	5	6	7	8
绝对湿度/(kg/m³)	40.8	41.5	39.9	39.5	32.7	24.9	19.8	19.3

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表 3 不同算法比较结果

Table 3. Comparison results of different algorithms

实际数据	GA-GM算法		GGWO-GM算法^[15]		改进GM算法^[16]
实际数据	预测数据	误差/%	预测数据	误差/%	预测数据	误差/%
40.8	40.80		40.80		40.80
41.5	47.26	13.88	43.81	5.58	42.32	1.99
39.9	38.53	−3.43	38.33	−3.93	37.30	−6.51
39.5	35.95	−8.98	33.52	−15.12	32.87	−16.77
32.7	32.63	−0.30	29.33	−10.31	28.97	−11.39
24.9	27.06	8.67	25.65	3.04	25.53	2.57
19.8	20.82	5.15	22.44	13.35	22.50	13.68
19.3	18.28	−5.28	19.63	1.72	19.83	2.79

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表 4 不同算法下的指标比较

Table 4. Comparison of indicators under different algorithms

指标	GA-GM算法	GGWO-GM算法^[15]	改进GM算法^[16]
MRE	5.71	6.63	6.96
MSE	6.80	7.82	9.15

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