| Mine belt transportation system is the key equipment of coal mining,which undertakes the important task of raw coal transportation.At present,the coal mine belt conveyor in China has basically realized automation and remote centralized control which has the functions of comprehensive protection of the belt conveyor,and is moving towards the direction of intelligent development.At the same time,with the gradually mature application of variable frequency speed regulation technology,it is possible to accurately and intelligently control the belt conveyor,in which the real-time monitoring of coal transportation is the premise of realizing the intelligent speed regulation of the belt conveyor.The sensor senses the coal flow volume parameter information on the upper part of the belt and dynamically evaluates the real-time coal flow,which can provide a scientific basis for the intelligent speed regulation of the belt machine,thus realizing the coal flow load balance in the whole mine.it is of great significance to improve the economic effect and safety production level of enterprises to reduce the invalid mode of key parts such as belt roller,save driving energy and improve the life of equipment.In this paper,based on the principle of laser measurement based on machine vision,a multi-parameter on-line measurement method of coal flow on belt conveyor is proposed,and the real-time coal quantity monitoring technology of belt conveyor is studied.The main research contents are as follows:(1)Aiming at the coal mine belt transportation system,the coal flow parameter measurement model and system design constraints are analyzed,and a set of vision measurement system is designed based on the principle of machine vision measurement system,including the selection and installation of hardware and the work flow of software.and develop a prototype.A calibration method of coal flow parameter measurement system based on HALCON calibration assistant is proposed to obtain the internal and external parameters of the camera,correct the lens distortion,and complete the conversion from pixel coordinates to world coordinates,which lays a foundation for the measurement of coal flow parameters on the belt conveyor.(2)The laser measurement algorithm of coal flow profile based on machine vision is studied.The laser stripe image is processed by image preprocessing,stripe effective region segmentation,centerline extraction,broken line repair and so on,and the commonly used laser centerline extraction methods and breakpoint connection techniques are compared.select the appropriate centerline extraction and breakpoint connection algorithm.The experimental results show that the algorithm can improve the extraction effect of fringe centerline and the measurement accuracy of coal flow cross-section parameters.(3)The real-time monitoring method of coal flow velocity of belt conveyor is studied.By comparing different sharpness evaluation functions,the evaluation function suitable for this experiment is selected.based on this method,the collected images are evaluated,and two frames are selected for coal flow velocity measurement.finally,the real-time migration velocity field of coal flow on the belt is calculated based on the target tracking method.(4)The key factors affecting the measurement accuracy are studied and analyzed.in order to solve the problem of image deterioration of visual system caused by high dust environment in coal mine,a film-uncovering dust cleaning device is designed,which is verified by laboratory simulation environment experiment.it can effectively improve the image quality and system measurement accuracy.A coal flow parameter monitoring system based on machine vision is developed.through the real-time crosssectional area and speed measurement of coal flow,the dynamic coal passing capacity of the belt is obtained,which provides the data basis for the intelligent speed regulation of the belt machine.This paper has 87 figures,14 tables and 121 references. |
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