Intelligent workshop data monitoring method

Abstract

The invention discloses an intelligent workshop data monitoring method, which comprises the following steps: a, acquiring workshop RFID tag original information, and generating an intelligent workshoporiginal tag event; b, preprocessing the data of the original label event of the intelligent workshop based on a data processing algorithm of an improved hash table to obtain a simple event of the intelligent workshop; c, enabling the data of the simple events of the intelligent workshop to flow into a workshop data disturbance event Esper monitoring system for filtering, and mining a logic relation between the simple events of the intelligent workshop; and d, combinging the intelligent workshop simple events through the relation operators to generate intelligent workshop complex events, andanalyzing and processing from the data streams to complete retrieval of the intelligent workshop complex events. According to the intelligent workshop data monitoring method, false positive errors caused by misjudgment due to the same label hash value can be completely avoided, and meanwhile, filter failure caused by increase of data streams can be effectively avoided.

Description

technical field [0001] The invention relates to an intelligent workshop data monitoring method, which belongs to the technical field of industrial software operation. Background technique [0002] Workshop data collection is the basis for subsequent data analysis work. Workshop disturbance events have always been the main reason affecting the normal work of the workshop. Efficient collection and monitoring of real-time and accurate data information of manufacturing resources provides a new solution for the response to disturbance events. research ideas. In recent years, with the rapid development of the Internet of Things and big data, the pace of workshop intelligence and informatization has been accelerated. The popularity of RFID technology in the Internet of Things undoubtedly provides great convenience for the collection and monitoring of workshop data. The manufacturing resources (people, workpieces, AGV, etc.) of the intelligent manufacturing workshop become intellig...

AI Technical Summary

Problems solved by technology

Therefore, when a manufacturing resource stays somewhere, the reader will generate a large amount of redundant data. In addition, due to unstable factors in the external environment, such as the instability of the radio frequency signal, the reader will generate missed readings or Mispronunciation

[0003] Bloom filter is an algorithm for filtering original redundant data of simple events, but traditional Bloom filter will generate false positive errors and the bit array will become invalid as the data flow increases, making it impossible to filter a large number of data for a long time. Limitations of RFID Data

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