基于差异照度的综放开采煤矸混合度精准识别研究

智能放煤是综放开采实现智能化的关键核心技术，而煤矸混合度的自动识别是制约智能放煤的主要理论和技术瓶颈。目前研究较多的震动信号识别法在煤矸力学性质相差不大时，易产生较大的识别误差。本项目结合图像识别原理和深度学习理论，提出采用差异照度法实现煤矸混合度精准识别的思路。一方面，研究煤矸图像的灰度、纹理以及分形等特征参量在不同照度下的响应差异，并基于此建立煤矸混合度识别深度学习算法；另一方面，通过数值仿真软件建立煤矸混合度识别可靠性检验、评价与反馈模型，结合室内试验结果，对所提出算法的可靠性进行综合评价并针对性优化，达到煤矸混合度精准识别的目的。本项目旨在为综放开采煤矸精准识别提供理论支撑，为自动化放煤成套设备的研发奠定基础，从而推动我国智能化综放开采技术瓶颈的突破。

Intelligent control of caving mining is the key technology to realize intellectualization in longwall top-coal caving mining, and automatic recognition of coal gangue mixture degree is the main theoretical and technical bottleneck restricting intelligent caving. At present, when the mechanical properties of coal and gangue are not different, the vibration signal recognition method which has been studied more often is liable to produce larger recognition errors. Based on the basic principle of image recognition and deep learning theory, the approch of accurate recognition of coal gangue mixture degree by differential illumination method is proposed in this project. On the one hand, the response differences of gray, texture and fractal characteristics of coal gangue image under different illumination are studied, and based on this, a deep learning algorithm for recognition of coal gangue mixture degree is established. On the other hand, the reliability test, evaluation and feedback model of coal gangue mixture degree recognition is established by numerical simulation software. Combined with the laboratory test results, the reliability of the proposed algorithm is evaluated comprehensively and optimized to achieve the purpose of precision recognition of coal gangue mixture degree. This project aims to provide theoretical support for the precision recognition of coal and gangue in longwall top-coal caving mining, lay the foundation for the development of automatic caving equipment, and promote the breakthrough of the bottleneck of intelligent longwall top-coal caving mining technology in China.