基于卡尔曼滤波的遗传蚁群混合算法优化改进云模型的渗流监测异常值识别
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云南农业大学

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TV698.1

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国家自然科学(52069029,52369026),云南省教育厅科学研究(2023J0519)


Identification of outliers for seepage monitoring with improved cloud model optimised by Kalman filter-based genetic ant colony hybrid algorithm
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Yunnan Agricultural University

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    摘要:

    大坝安全监测序列中广泛分布异常值,对其进行筛选与辨识是判定大坝运行性态的前提。传统的基于回归模型的异常识别方法会对监测数据造成正常值误判或者异常值漏判的情况。针对上述问题,将监测数据序列结合卡尔曼滤波方法去除噪声项,并以测值的日变化速率代替去噪后的数据,从而保留数据真实的演变轨迹,再结合云模型,建立基于日变化速率的改进云模型。同时采用遗传蚁群混合算法对改进云模型的阀值进行优化。分别对去噪前后和阀值优化前后的异常值数量进行对比分析。结果显示:原始数据经过卡尔曼滤波去噪处理后,日变换速率的总体范围显著减小,而用遗传蚁群混合算法对阀值区间进行优化后,其优化后的阀值区间小于优化前的。结果表明:本文提出的方法在大坝的渗流监测中可更好的识别异常值,减少因噪声而引起的误判,有效提高对异常值的识别精度。

    Abstract:

    Anomalies are widely distributed in dam safety monitoring sequences, and their screening and identification is a prerequisite for determining the operational state of a dam. The traditional regression model-based anomaly identification method may cause misjudgment of normal values or omission of anomalies in the monitoring data. To address the above problems, the monitoring data sequence is combined with Kalman filtering method to remove the noise term, and the daily change rate of the measured value is used to replace the denoised data, so as to retain the real evolution trajectory of the data, and then combined with the cloud model, the improved cloud model based on the daily change rate is established. At the same time, a genetic ant colony hybrid algorithm is used to optimise the threshold value of the improved cloud model. The number of outliers before and after denoising and before and after threshold optimisation are compared and analysed respectively. The results show that the overall range of the daily transformation rate is significantly reduced after the raw data has been processed by Kalman filter denoising, while the optimisation of the threshold intervals with the Genetic Ant Colony Hybrid Algorithm shows that its optimised threshold intervals are smaller than the pre-optimisation ones. The results show that the method proposed in this paper can better identify the outliers in the seepage monitoring of dams, reduce the misjudgement caused by noise, and effectively improve the identification accuracy of the outliers.

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王奎,欧斌,刘振宇,等. 基于卡尔曼滤波的遗传蚁群混合算法优化改进云模型的渗流监测异常值识别[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-09-22
  • 最后修改日期:2024-05-23
  • 录用日期:2024-05-29
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