Coal briquette transportation monitoring method and device

Abstract

The embodiment of the invention provides a coal briquette transportation monitoring method and device. The method comprises the steps of determining real-time coordinate information of a scraper assembly, in a coal briquette transportation area, of a scraper conveyor based on target video data, continuously collected in real time, of the coal briquette transportation area; inputting target image data corresponding to the target video data into a large coal recognition model, and obtaining a current large coal recognition result according to the output of the target image data; and if the result contains the large coal, judging whether the large coal is located on the scraper conveyor or not according to the scraper assembly and the real-time coordinate information of the large coal in the large coal recognition result, and if yes, obtaining the motion state of the large coal to determine whether transportation blocking early warning is conducted on the large coal or not. According to the coal briquette transportation monitoring method and device provided by the invention, the recognition precision of the large coal can be effectively improved, the recognition accuracy of the area where the large coal is located can be effectively improved, and the accuracy of obtaining the motion state of the large coal can be improved, so that the accuracy of coal briquette transportation monitoring and blocking monitoring early warning is improved.

Description

technical field [0001] The present application relates to the technical field of coal block identification, in particular to a coal block transportation monitoring method and device. Background technique [0002] With the increase of the mining height and mining intensity, the coal seam of the fully mechanized mining face with large mining height will also increase the large coal lumps. The large coal lumps are super large irregular blocks, which are prone to blockage when transported on the conveying and reloading equipment. If a blockage occurs, the subsequent coal flow at the blockage position will quickly and continuously accumulate, which will cause overloading, jamming and shutdown of the conveying and reloading equipment, which will seriously affect production and bring huge safety hazards to personnel and equipment. In response to this problem, image recognition can be used to identify large coal blockage scenes, and real-time early warning signals can help coal mine...

AI Technical Summary

Problems solved by technology

[0003] At present, the existing identification method of blockage of large coal is usually as follows: collect the coal flow in transportation through image recognition, and obtain the area of ​​large coal according to the motion characteristics and brightness characteristics of the image. This kind of error is relatively large. The location is inaccurate, and using the large coal identified in the static picture scene to do the blockage warning will lead to a large number of false alarms. Based on this, video recognition can also be considered based on the diameter of the coal block and the width of the scraper conveyor. Blockage identification of large coal, however, only by the size of the diameter of the coal and the width of the scraper conveyor to judge whether it is a large coal, this method seems to be correct, but it is really unreasonable, that is, if the diameter of the coal is larger than The width of the scraper, but it does not affect the operation of the scraper, so no blockage will occur. If the coal block is identified as a large block of coal with blockage, the reliability of the coal block blockage warning will be reduced.

[0004] That is to say, no matter what kind of existing coal transportation monitoring method, there are problems such as inaccurate identification area of ​​large coal, inaccurate judgment of motion state, resulting in poor accuracy of blockage judgment, etc.

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