Vision-based lightweight capacity management and control method under industrial Internet of Things

Abstract

The invention discloses a vision-based lightweight capacity management and control method under an industrial internet of things, which comprises an edge computing intelligent box control system and an edge service system; one or more cameras and one or more edge computing intelligent boxes are deployed on a production line; the camera sends a binding request, the edge calculation intelligent box receives the request, and the edge calculation intelligent box is bound with the camera; the edge computing intelligent box sends an initialization configuration request, and the edge service system receives the request; the edge computing intelligent box obtains a corresponding model address from the edge service system and downloads a model; the edge computing intelligent box obtains edge computing intelligent box configuration information from the edge service system; the edge calculation intelligent box intercepts real-time video stream data sent by the camera and operates the model to obtain a model identification result; and the edge computing intelligent box reports a model identification result to the edge service system, and the edge service system receives the model identification result and processes the model identification result. According to the method, the efficiency of the digital factory is improved, the cost is reduced, and the productivity management and control level is improved.

Description

technical field [0001] The invention relates to the technical field of production capacity management and control of digital factories, in particular to a vision-based lightweight production capacity control method under the Industrial Internet of Things. Background technique [0002] At present, in the complex industrial Internet of Things scenario, the usual solution to the production capacity control problem of digital factories is to connect with third-party systems (such as MES, CRM, ERP and other information systems). This solution, to a certain extent, can solve problems in digital factories such as output statistics, statistics of good products, real-time statistics of equipment status, whether the machine or manual operation is standardized, etc., but this production capacity control method cannot significantly improve the production capacity control of digital factories. The level cannot improve the efficiency of the digital factory, and it also increases the cost ...

AI Technical Summary

Problems solved by technology

This solution, to a certain extent, can solve problems in digital factories such as output statistics, statistics of good products, real-time statistics of equipment status, whether the machine or manual operation is standardized, etc., but this production capacity control method cannot significantly improve the production capacity control of digital factories. Level, can not improve the efficiency of the digital factory, but also increases the cost of the digital factory

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