Online pipe network anomaly detection system based on machine learning

Abstract

The invention discloses an online pipe network anomaly detection system based on machine learning. The online pipe network anomaly detection system comprises a data collection unit, a data distribution unit and a plurality of anomaly detection units. The data collection unit is used for collecting real-time data of an online pipe network, merging the real-time data according to position areas and grouping the real-time data into different data packages. The data distribution unit is used for receiving the data packages, extracting data elements from the data packages and dividing the data packages into a plurality of data subsets after formatting the data packages. The anomaly detection units are used for receiving the data subsets in a one-to-one correspondence mode and predicating anomalism of the data subsets based on a semi-supervised machine learning framework. The anomaly detection units can be used for carrying out parallel data processing, and data transmission can be carried out among the anomaly detection units through an MPI. The online pipe network anomaly detection system can meet the requirements of the online anomaly detection units based on machine learning for usability of a server, and can prevent extra hardware on standby in an idle state from being introduced in.

Description

technical field [0001] The invention relates to a facility pipe network monitoring technology, in particular to an online pipe network anomaly detection system based on machine learning. Background technique [0002] The development of sensor technology enables sensors to measure parameters with high spatio-temporal precision in the environmental field. The time-series data collected by the sensors is continuously input into the storage to form a data stream. Taking water supply system management as an example, sensor data can include various hydraulic parameters and water quality indicators. These data can be used for anomaly detection, etc., which distinguishes data anomalies through historical patterns or model predictions. An abnormal condition may be a pipeline leak or a contamination incident. The large geographic scale of pipelines and the high complexity of water use characteristics caused by weather changes, seasonal changes, holidays, and changes in community de...

AI Technical Summary

Problems solved by technology

The large geographical scale of the pipeline and the high complexity of water use characteristics caused by weather changes, seasonal changes, holidays, and community demographic changes make manual methods unsuitable for the job

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