Abstract
In order to solve the prewarning problem of South-to-North Water Transfer Project safety, an intelligent cooperative prewarning method based on machine learning was proposed under the framework of intelligent information processing. Driven by the monitoring data of the South-to-North Water Transfer Project, the single sensor in typical scenes was studied, and the security threshold was predicted along the vertical axis of time, firstly. With the support of the data correlation calculation, the sensors in the typical scene were intelligently grouped, and the study objectives were changed into sensor grouping, secondly. Then, the nonlinear regression model between the single sensor and the multisensors was built on the time cross section, and the model was used to dynamically calculate the safety threshold of the current sensor for the second time. Finally, in the framework of intelligent information processing, a double verification mechanism was proposed to support the construction of the intelligent prewarning method for the safety of South-to-North Water Transfer Project. The paper collected the monitoring data from November 2015 to September 2016 in the typical scenarios. The experimental results showed that the methods constructed in the paper can be able to identify the abnormal causes of data sudden jump effectively and give the different level prewarning. The method provides a strong theoretical support for further manual investigation work.
Highlights
In order to solve the prewarning problem of South-to-North Water Transfer Project safety, an intelligent cooperative prewarning method based on machine learning was proposed under the framework of intelligent information processing
When the monitoring data was abnormal, we cannot judge whether the data anomaly was caused by sensors or by the channel engineering failure; the staff could not grasp the overall situation of the channel security. In view of this problem, under the driving of the safety monitoring data of the South-to-North Water Transfer Project, the paper regarded a single sensor as research object and predicted the safety threshold along the time axis by Kalman filter method based on its historical monitoring data, firstly; the paper expanded the research object from single sensor to sensor grouping by using the data correlation analysis methods, which can reduce the computational complexity and improve the accuracy of prediction algorithm
The intelligent prewarning method was constructed under the framework of intelligent information processing, which provided scientific theoretical support and effective decisionmaking for the emergency troubleshooting and emergency countermeasures
Summary
The middle route of the South-to-North Water Transfer Project diverted water from the Dan Jiang Kou reservoir to Henan Province, Hebei Province, Tianjin, and Beijing. The engineering safety referred to the safety problems of the middle route of the South-to-North Water Transfer Project mainly including the buildings, channels, and the important engineering facilities. When the monitoring data was abnormal, we cannot judge whether the data anomaly was caused by sensors or by the channel engineering failure; the staff could not grasp the overall situation of the channel security In view of this problem, under the driving of the safety monitoring data of the South-to-North Water Transfer Project, the paper regarded a single sensor as research object and predicted the safety threshold along the time axis by Kalman filter method based on its historical monitoring data, firstly; the paper expanded the research object from single sensor to sensor grouping by using the data correlation analysis methods, which can reduce the computational complexity and improve the accuracy of prediction algorithm. The algorithm was validated and the results were analyzed
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