SOA-architecture-based method for device data collecting and monitoring early warning

Abstract

The invention relates to a SOA-architecture-based method for device data collecting and monitoring early warning. Based on basic primitives of a static model, the mode of visualized images is used, through relevance and incidence relations among dynamic allocation primitives, a group organization chart and a department physical layout chart are formed, and a visual and configurable digitalized factory model is constructed. Three basic semantics elements including a group, a branch and a department are extracted from organizations related to production, through configuration of basic connection relations, and an organization structure model supporting a group-branch structure is formed. Three basic semantics elements including a production area, a production position and processing equipment are extracted from the physical layout related to factory practical production, and through configuration of and-or-invert ternary relations, a physical layout model and physical connection relations of the model are built. Underlying data collection and control are extracted to be collecting variable elements, and through configurations of a data source and an OPC communication protocol, bi-directional communication between underlying physical equipment and an equipment collecting and monitoring model is achieved.

Description

technical field [0001] The invention mainly relates to a modeling method for equipment data collection, monitoring and early warning, in particular to a modeling method based on visual graphics. Background technique [0002] While customers have higher and higher requirements for product quality, they also put forward higher requirements for the management of the equipment's own state parameters and processing parameters in the production and processing process. Due to the lack of effective automatic collection capabilities and effective integration means, many enterprises adopt the method of manually collecting relevant data in the processing link and entering it into the corresponding business system. Due to the huge amount of data, the labor intensity is high, the efficiency is low, and it is easy to generate wrong data or incomplete dirty data, and the manual statistics often take a long time, which makes misjudgments and inaccuracies often occur in the process of interp...

AI Technical Summary

Problems solved by technology

Due to the lack of effective automatic collection capabilities and effective integration means, many enterprises adopt the method of manually collecting relevant data in the processing link and entering them into the corresponding business system

Due to the huge amount of data, the labor intensity is high, the efficiency is low, and it is easy to generate wrong data or incomplete dirty data, and the manual statistics often take a long time, which makes misjudgments and inaccuracies often occur in the process of interpreting equipment failures and product quality. Timely and other phenomena make the equipment work in an abnormal state, and it is difficult to guarantee product quality

[0003] If an enterprise lacks an effective and integrated unified platform for data collection, statistical monitoring, and early warning, and lacks effective countermeasures for massive data collection and monitoring, then there will be a certain lag in each process, making it difficult to guarantee the safety of equipment failures. When there is an abnormal trend in the early stage and product quality, find and solve the problem in time

This greatly increases the intangible production cost of the enterprise

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