| 基于多点关联性的尾矿坝位移监测序列异常值诊断* |
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| 引用本文： | 易思成,康喜明,吴浩,胡少华.基于多点关联性的尾矿坝位移监测序列异常值诊断*[J].中国安全生产科学技术,2022,18(6):45-51. |
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| 作者姓名： | 易思成 康喜明 吴浩 胡少华 |
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| 作者单位： | (1.武汉理工大学 安全科学与应急管理学院，湖北 武汉 430070；2.国网内蒙古东部电力有限公司，内蒙古 呼和浩特 010020；3.华中师范大学 城市与环境科学学院，湖北 武汉 430079) |
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| 基金项目： | * 基金项目: 国家自然科学基金项目(51979208)；2019年湖北省技术创新专项重大项目(2019ACA143) |
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| 摘 要： | 针对尾矿坝位移监测序列中噪声和真实异常值的区分问题，提出1种基于多点关联性和改进孤立森林（IF）算法的异常数据诊断模型。通过IF算法对监测序列中的各样本点异常程度进行量化计算，引入云模型（CM）算法确定IF量化的异常得分与异常概念的相互映射关系以实现异常点的初步诊断，根据Apriori算法计算多测点序列间的关联性，找出强关联序列组合，结合序列关联性以及异常点诊断结果区分噪声与真实异常值。以某尾矿坝位移监测序列为例进行模型验证。研究结果表明:基于多点关联性的异常诊断模型能够有效区分尾矿坝位移监测序列中的噪声与真实异常值，提高监测系统的准确性。
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| 关 键 词： | 监测序列 关联性 孤立森林 异常诊断模型 |
Diagnosis on abnormal values in displacement monitoring sequence of tailings dam based on multi-point correlation |
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| YI Sicheng,KANG Ximing,WU Hao,HU Shaohua.Diagnosis on abnormal values in displacement monitoring sequence of tailings dam based on multi-point correlation[J].Journal of Safety Science and Technology,2022,18(6):45-51. |
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| Authors: | YI Sicheng KANG Ximing WU Hao HU Shaohua |
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| Affiliation: | (1.School of Safety Science and Emergency Management,Wuhan University of Technology,Wuhan Hubei 430070,China;2.State Grid Inner Mongolia East Electric Power Co.,Ltd.,Hohhot Inner Mongolia 010020,China;3.College of Urban and Environmental Sciences,Central China Normal University,Wuhan Hubei 430079,China) |
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| Abstract: | Aiming at the problem in distinguishing the noise and real abnormal values in the displacement monitoring sequence of tailings dam,a diagnosis model of abnormal data based on the multi-point correlation and improve isolated forest (IF) was put forward.The abnormal degree of each sample point in the monitoring sequence was quantitatively calculated by using the IF algorithm,and the cloud model (CM) algorithm was introduced to determine the mutual mapping relationship between abnormal score and abnormal concept of IF quantification,so as to realize the preliminary diagnosis of abnormal points.The correlation between multi-point sequences was calculated according to the Apriori algorithm,then the strong correlation sequence combination was found out,and the noise and real abnormal values were distinguished combining with the sequence correlation and the diagnosis results of abnormal points.The model validation was conducted by taking the displacement monitoring sequence of a tailings dam as example.The results showed that the anomaly diagnosis model based on multi-point correlation could effectively distinguish the noise and real abnormal values in the displacement monitoring sequence of tailings dam,thus improve the accuracy of monitoring system. |
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| Keywords: | monitoring sequence correlation isolated forest (IF) anomaly diagnosis model |
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