Detection method of abnormal event of multi-variable water quality parameter time sequence data

Abstract

The invention discloses a detection method of an abnormal event of multi-variable water quality parameter time sequence data. The detection method comprises the following steps: firstly, inputting a plurality of water quality parameter models; training and constructing a data driven predication model (BP model); analyzing multi-variable water quality time sequence data in a water supply pipe net and estimating the model; secondly, predicating through the BP model to obtain a predicated value of water quality data; comparing an actually measured value of a current state and the predicated value obtained by the predication model and carrying out error estimation and classification analysis, so as to determine a single-variable parameter abnormal event; classifying based on an error counting result, and updating and determining the event probability of single-variable water quality parameters through sequential Bayesian updating; carrying out multi-variable fusion decision-making and fusing information from a plurality of water quality monitoring indexes; providing a unified decision-making result and determining whether the water supply pipe net has the abnormal event on a specific node or not.

Description

technical field [0001] The invention relates to a method for detecting abnormal events of time series data of multivariable water quality parameters and the technical field of water supply network monitoring network. Background technique [0002] With the rapid economic development, abnormal water pollution incidents occur frequently. If a city's water supply system is polluted, it will bring huge losses to the society. Pollution events in the water distribution network spread rapidly and cause more damage. Therefore, timely detection of abnormal water quality events in the water supply network, timely warning of abnormal pollution events, and prevention of continued spread of pollution are of great practical significance. At present, wireless sensor networks are widely used in the monitoring of various scenarios. The parameters that can clearly reflect the water quality mainly include free chlorine, total organic carbon, electrical conductivity, pH value, temperature, and...

AI Technical Summary

Problems solved by technology

[0004] The above methods are all limited to judging abnormal water quality pollution events in the water supply pipe network based on whether a single water quality index exceeds the standard, and the single detection index does not conform to the real water supply pipe network water environment.

When a pollution event occurs in the water supply pipe, multiple parameters will change significantly, and the single factor considered for a single parameter is likely to cause missed and false positives, and the detection accuracy is not high

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