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Early warning threshold setting method for dam safety monitoring data exception identification

A security monitoring and data anomaly technology, applied in complex mathematical operations, climate change adaptation, etc., can solve problems such as misjudgment and missed judgment of normal measured values

Active Publication Date: 2020-09-11
SICHUAN UNIV
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Problems solved by technology

[0006] The technical problem to be solved by the present invention is to provide an early warning threshold setting method for abnormal identification of dam safety monitoring data, solve the problems of misjudgment of normal measured values ​​and missed judgment of abnormal measured values, improve the accuracy of online identification of data abnormalities, reduce data Misjudgment rate of abnormal online identification

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  • Early warning threshold setting method for dam safety monitoring data exception identification
  • Early warning threshold setting method for dam safety monitoring data exception identification
  • Early warning threshold setting method for dam safety monitoring data exception identification

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Embodiment Construction

[0049] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The process of the present invention applied to the abnormal online identification of actual engineering monitoring data is as follows: figure 1 , through the following steps:

[0050] 1. Construct a robust regression model based on Tukey's double weight estimation function.

[0051] The regression model established through the historical effect size and environmental quantity is shown in the following formula (1):

[0052] Y=Xβ+μ (1)

[0053] In the formula, Y is the effect size vector composed of historical observations; X is the historical environmental quantity variable matrix composed of water level factors, rainfall factors, temperature factors, and aging factors; β is a coefficient vector; μ is a random error term.

[0054] Using the M estimation method to solve the robust regression model, the coefficient vector The estima...

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Abstract

The invention discloses an early warning threshold setting method for dam safety monitoring data exception identification, and relates to the field of dam safety monitoring, and the method comprises the following steps: constructing a robust regression model based on a graph-based double-weight estimation function; on the basis of the robust regression model, replacing the residual standard deviation S with a scale estimator ST based on a position M estimator; constructing a predicted value confidence interval radius D based on a robust regression model, wherein the abnormal early warning threshold value is set to be [-3ST-D, 3ST + D]. According to the method, the accuracy of data exception online identification is improved, and the misjudgment and missed judgment rate of data exception online identification is reduced.

Description

technical field [0001] The invention relates to the field of dam safety monitoring, in particular to a method for setting an early warning threshold for abnormal identification of dam safety monitoring data. Background technique [0002] In dam safety monitoring, sudden changes in monitoring data are often a direct indication of changes in dam structural behavior. Accurate online identification of abnormal sudden changes in measured values ​​is a key issue in the intelligent management and control of dam operation safety. At present, there are many methods for abnormal identification of dam safety monitoring data, including Lait criterion method, statistical regression model, catastrophe theory, fuzzy cluster analysis, etc. The statistical regression model method based on Lait’s criterion is most commonly used in the online identification of dam safety monitoring data anomalies because it can comprehensively reflect the impact of environmental quantities, is convenient to ca...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/10
CPCG06F17/10Y02A10/40
Inventor 李艳玲陈建康吴震宇沈定斌张瀚黄会宝裴亮李兴
Owner SICHUAN UNIV
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