Monitoring method of strip mine excavator loading process based on images and apparatus thereof

Abstract

An embodiment of the invention provides a monitoring method of a strip mine excavator loading process based on images and an apparatus thereof. The method comprises the following steps: collecting video signals of the excavator loading process through using an image acquisition sensor; the collected video signals are converted into digital images that can be read by a digital signal processor (DSP); sending the digital images to the DSP; measuring a rotation angle of an excavator bucket during the loading process by using a angle sensor and sending to the DSP; receiving the digital image information and the angle measuring information by the DSP, comparing the received digital image information and the angle measuring information, matching processing and calculating so as to obtain a corresponding monitoring result. Therefore, automatic monitoring of the strip mine excavator loading process can be realized. Corresponding manpower and material resources are saved and strip mine loadingtechnology efficiency can be raised.

Description

technical field [0001] The invention relates to the technical field of open-pit mine excavators, in particular to an image-based monitoring method and device for the loading process of an open-pit mine excavator. Background technique [0002] At present, in the process of open-pit mine production, the process of excavator loading trucks or electric locomotives is called shovel loading process, which is one of the most important links in open-pit mine production. [0003] In the prior art, indicators such as the number of excavators loading trucks or locomotives, the number of trucks loaded per shift, the degree of fullness (volume or weight) of each truck, and the loading time cannot be automatically statistically assessed. Manual counting can be used for statistics. However, there are many shortcomings in manual statistics: first, it requires a certain amount of manpower and material resources to operate, such as using specialized statisticians to do this work; It caused ...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, indicators such as the number of excavators loading trucks or locomotives, the number of trucks loaded per shift, the degree of fullness (volume or weight) of each truck, and the loading time cannot be automatically statistically assessed. Able to use manual counting method to count

However, there are many shortcomings in manual statistics: first, it requires a certain amount of manpower and material resources to operate, such as using specialized statisticians to do this work; It caused problems such as ore loss, output loss, and unbalanced loading and unloading quantities, which affected the efficiency of the shoveling process in open-pit mines, and could not effectively supervise the shoveling process

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