Video-based automatic identification system for abnormal conditions of fused magnesium furnace

Abstract

The invention relates to a video-based automatic identification system for abnormal conditions of a fused magnesium furnace. The method includes the steps of acquiring fused magnesia production fieldcondition information through an image information acquisition module and transmitting the information to a sample generation module; generating a training sample by the sample generation module, performing label processing and manual classification label processing on images through label-image software, and transmitting the processed video and image information to a detection and classificationmodule; performing, by the detection and classification module, feature extraction and processing on the video and image information through a corresponding algorithm to obtain an intelligent detection and classification model of the condition identification system; and displaying, by a display unit, the novel uses the test video and image information in a visualized manner by using the intelligent detection and classification model processed by the detection and classification module. The video-based automatic identification system of the invention has obviously improved identification effects as compared to a system using non-visual information alone such as the current and voltage, is reasonable in hardware device structure, convenient in operation, low in cost and high in the accuracyof discrimination and can replace manual routing inspection.

Description

technical field [0001] The invention relates to an equipment working condition recognition system in the field of pattern recognition and artificial intelligence technology, in particular to a video-based automatic recognition system for abnormal working conditions of a fused magnesium furnace. Background technique [0002] Fused magnesia has the characteristics of high melting point, compact structure, strong oxidation resistance, high compressive strength, strong corrosion resistance, stable chemical properties, etc. It is an important strategic raw material. [0003] In our country, the preparation of high-grade fused magnesia is mainly completed by electric arc furnace melting and recrystallization. The smelting process of raw material magnesia sand in the furnace can be artificially divided into working conditions such as furnace start-up, feeding, normal smelting, under-burning and abnormal exhaust. Among them, under-burning and abnormal exhaust conditions belong to a...

AI Technical Summary

Problems solved by technology

The main problems of manual inspection are: 1) The accuracy of the judgment is related to the experience and status of the operator, and it is easy to miss and falsely detect; 2) The on-site production environment is harsh (strong light, high temperature, dust, steam, etc.), labor High strength, high risk, not suitable for long-term on-site inspection by workers

[0005] The previous automatic identification technology was judged by the electrode current and voltage collected in real time, but the accuracy was low and the degree of visualization was weak, so it could not replace manual observation

This is because the visual characteristics of on-site working conditions are still the fastest and most reliable basis for judging abnormal working conditions, and the fluctuation of current and voltage can only assist in judging

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