Water quality abnormal event identification and early warning method based on pipe network multivariate water quality time series data

Abstract

The invention discloses a water quality abnormal event identification and early warning method based on pipe network multivariate water quality time series data, and belongs to the technical field ofwater supply pipe network water treatment. Firstly, water quality data, collected by SCADA, of monitoring points are preprocessed, and simulated water quality abnormal event data are simulated; and secondly, a prediction regression model is established for a plurality of water quality indexes in the normal operation state, well-trained regression prediction models of each water quality index are integrated to construct a final regression prediction model. Thirdly, a standard deviation of residual distribution of a predicted value and a true value of each water quality index is determined, theregression prediction model is evaluated, and an optimal arithmetic multiplier is determined. Finally, the probability of the water quality abnormal event is updated by utilizing a time sequence Bayesian principle, an event alarm is given, and an alarm signal of a final model, the occurrence probability of the water quality abnormal event and an abnormal water quality index are given. The method has the advantages of low operation cost, simple operation, good effect and the like, and can greatly reduce the false alarm rate and the missing report rate.

Description

technical field [0001] The invention relates to the technical field of water treatment of water supply pipe networks, in particular to an abnormal event identification and early warning method based on multivariate water quality time series data of the pipe network. Background technique [0002] The water distribution pipe network is directly facing the users and is an important part of the water supply system. The cleanliness of drinking water for residents directly affects the health of the people. After a water pollution event occurs in the pipe network, the pollutants will quickly spread in the pipe network with the movement of water quality, which will not only cause huge economic losses and affect the safety of water supply, but also cause environmental damage, affect social order, and even threaten to the safety of residents. [0003] Water supply network celebrities and frequent yellow water accidents have threatened the water safety of residents. Therefore, the rap...

AI Technical Summary

Problems solved by technology

There are certain deficiencies in the current research, and the water quality events of the unstable water quality event sequence cannot be well identified. In the practical application of the pipe network, a large number of parameters need to be calibrated, and the pipe network systems in different regions are measured by online water quality monitoring platforms. The water quality indicators are different, and the measurement accuracy of water quality data is also different. It is more troublesome to apply to the pipeline network in different regions, and the probability of correct early warning is not high.

At the same time, during the actual operation of the pipeline network, the water quality data in the pipeline network may often fluctuate abnormally due to the influence of sensors or changes in the operating conditions of the pumps. For abnormal events, a large number of studies identify abnormal points of water quality and carry out early warning reports of water quality, with a high rate of false alarms and false positives

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