Dynamic production environment anomaly monitoring system oriented to real-time data streams

Abstract

The invention relates to a dynamic production environment anomaly monitoring system oriented to real-time data streams. The dynamic production environment anomaly monitoring system comprises a real-time data collection module (I), a single-node sparse discrete encoder generation module (II), a single-node real-time anomaly monitoring module (III), a full-process all-node parallel real-time monitoring module (IV) and a production mode updating module (V), wherein the I is used for collecting numerical values of all nodes and integrating the numerical values into a multi-dimensional sample; theII is used for generating a plurality of sparse discrete encoders, wherein each sparse discrete encoder is used for performing sparse discrete encoding on the numerical value of each node; the III isused for judging whether the value is abnormal; the IV is used for monitoring all nodes in parallel in real time, and identifying noise interference and concept drift of each node according to the decision matrix; and the V is used for changing the production mode when it is determined that concept drift occurs. The dynamic production environment abnormity monitoring system oriented to the real-time data flow can monitor system abnormity in real time and distinguish whether noise interference or production mode change exists from the system abnormity.

Description

technical field [0001] The invention belongs to the technical field of industrial automation and intelligent manufacturing, and relates to a real-time data flow-oriented dynamic production environment abnormality monitoring system. Background technique [0002] In the intelligent production process, data is the core element of monitoring and driving the production process. The noise in real-time production data is often caused by various physical disturbances outside the production system, which usually does not cause changes in the overall trend of the data. The change in the overall trend of the data is generally caused by the change in the production mode within the production system, which is manifested in the data as a trend change, which generally includes two situations: sudden change and gradual change. Academically, this phenomenon of trending data changes is called concept drift. At present, the research on concept drift mainly focuses on drift detection and adapt...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that it is difficult to distinguish noise interference from drift data in the prior art, and to provide a dynamic production environment anomaly monitoring system oriented to real-time data flow

Images