Smart factory remote monitoring method and system based on 5g and algorithms

Abstract

The invention belongs to the technical field of equipment operation status monitoring, and specifically relates to a 5G-based smart factory remote monitoring method and system, including multiple working areas in the factory, and an image acquisition module in each working area. The image acquisition module described above acquires the operating status data of the equipment in the work area at predetermined intervals; uses the Otsu algorithm and the particle swarm algorithm to segment the received image to obtain the target image in the equipment image; Compare and analyze the characteristic information of the abnormal operation of the production equipment in the abnormal event database to determine whether the production equipment is faulty. If so, it is determined that there is an abnormal event in the production equipment; And according to the fault level, the corresponding exception handling strategy is obtained; it has the beneficial effect of improving the speed and accuracy of image segmentation and improving the reliability of equipment operation status monitoring.

Description

technical field [0001] The invention belongs to the technical field of equipment operation state monitoring, and in particular relates to a remote monitoring method and system for smart factories based on 5G and algorithms. Background technique [0002] Among all production safety accidents, safety accidents in factories have obvious characteristics. Fires and explosions in factories not only often cause casualties and heavy property losses, but also easily lead to serious environmental problems, affecting residents and construction in nearby areas. [0003] At present, wireless monitoring technology is often used for factory safety monitoring. Video sensors are arranged at specific locations to monitor equipment or scenes in the factory, and use radio waves to transmit video or image data, and transmit live images at key locations to Terminals, and can automatically alarm for emergencies. [0004] In this type of wireless monitoring system, the video sensor is responsible ...

AI Technical Summary

Problems solved by technology

[0006] The maximum inter-class variance method is also called the Otsu method. It is a very simple and general method, and its biggest feature is that it can realize the self-adaptation of image segmentation. Its basic principle is: by finding the two parts that make the background and the target The variance between can reach the maximum threshold, and then use this threshold to separate the image target from the background. However, the existing Otsu method cannot accurately keep the boundary shape intact when there is exposure and the contrast between the background and the target is low. Segmentation accuracy is low

Images