Research progress on intelligent coal caving theory and technology
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摘要: 综放开采技术是厚及特厚煤层开采的有效方法，已成为我国在世界煤炭开采行业的标志性技术。综述了“四要素”放煤理论、顶煤采出率与含矸率关系、基于块度分布的采出率预测模型、煤流瞬时含矸率−累计含矸率关系等智能放煤理论研究进展。分析了智能放煤技术难点，指出含矸率是影响顶煤采出率和煤质的关键因素，放煤过程中含矸率的快速、准确计算是智能放煤技术突破的重点和关键。将智能放煤技术分为非图像识别智能放煤技术和图像识别智能放煤技术2类，对不同技术的研究进展、优缺点及使用条件进行了详细分析。非图像识别智能放煤技术包括记忆放煤技术、声音振动信号识别技术、γ射线探测技术、探地雷达技术、微波照射+红外探测技术、激光扫描放煤量监测技术等，图像识别智能放煤技术包括井下照度环境精准控制、放煤图像去尘算法、含矸率计算精度保障策略、煤岩红外图像识别等。Abstract: The longwall top-coal caving technology is an effective method for extracting thick and ultra-thick coal seams, and it has become a hallmark technology in China's coal mining industry. This paper reviews the research progress on the "Four elements" coal caving theory, the relationship between the top coal recovery rate and the rock mixed ratio, a recovery rate prediction model based on block distribution, and the relationship between instantaneous rock mixed ratio and cumulative rock mixed ratio. The challenges of intelligent coal caving technology are analyzed, emphasizing that the rock mixed ratio is a key factor affecting the top coal recovery rate and coal quality. Rapid and accurate calculation of the rock mixed ratio during the coal caving process is crucial for breakthroughs in intelligent coal caving technology. This technology is categorized into two types: non-image recognition and image recognition. The research progress, advantages, disadvantages, and usage conditions of different technologies are discussed in detail. Non-image recognition intelligent coal caving technology includes memory coal caving technology, sound and vibration signal detection technology, γ-ray detection technology, ground penetrating radar technology, microwave irradiation combined with infrared detection technology, and laser scanning coal caving monitoring technology. Image-based intelligent coal caving technology encompasses precise control of underground illumination environment, dust removal algorithms for coal caving images, accuracy assurance strategies for rock mixed ratio calculations, and infrared image recognition of coal and rock.
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图 2 综放工作面顶煤采出率与含矸率的关系
Figure 2. The relationship between top coal recovery rate and gangue content rate in fully mechanized top-coal caving face
图 3 瞬时含矸率与累计含矸率监测结果
Figure 3. Monitoring results of instantaneous and cumulative gangue content rates
图 4 投影面积含矸率与累计体积含矸率关系
Figure 4. Relationship between projected area and cumulative volume gangue content rates
图 6 不同照度、感光度及快门速度下的图像灰度特征
Figure 6. Image grayscale characteristics under different illuminance, sensitivity, and shutter speed
图 7 放煤图像去尘前后效果对比
Figure 7. Comparison of dust removal effects before and after processing coal caving images
表 1 智能放煤方法分类及其研究情况
Table 1. Classification and research status of intelligent coal caving methods
技术
类别技术 主要研究组织（按笔画排序） 非图像
识别
技术记忆放煤技术 大同煤矿集团有限责任公司[17]
山东能源集团有限公司[7]
天地科技股份有限公司[18-19]
中国矿业大学[20]
中国矿业大学（北京）[21]
中矿先进（北京）采矿技术研究院有限公司[21]
中煤华晋集团有限公司[22]
中煤科工开采研究有限公司[19, 23]
北京天地玛珂电液控制系统有限公司[22]
国家能源集团[21]
英国利兹大学[21]
陕西未来能源化工有限公司[23]
煤炭科学研究总院开采研究院[19, 23]声音振动信号识别技术 山东大学[24-26]
山东科技大学[27-30]
山东工商学院[31]
山东交通学院[25-26]
中国矿业大学[10, 32-36]
中国矿业大学（北京）[14]
天地科技股份有限公司[37]
太原理工大学[38]
北京天地玛珂电液控制系统有限公司[39]
北京信息科技大学[40]
辽宁工程技术大学[41]
潞安矿业（集团）有限责任公司[31]γ射线探测
技术中国矿业大学[13, 35, 42-44]
安徽理工大学[44]探地雷达技术 山东工商学院[45]
太原理工大学[38]
中煤华晋集团有限公司[46]
北京天地玛珂电液控制系统有限公司[45-46]
南阳理工学院[45]微波照射+红外探测技术 河南理工大学[8] 激光扫描放煤量监测技术 中国矿业大学[47-50]
应急管理部信息研究院[47-50]图像
识别
技术可见光图像识别技术 中国矿业大学（北京）[14-15, 21, 51]
中矿先进（北京）采矿技术研究院有限公司[21]
辽宁工程技术大学[41]
西安科技大学[52]
国家能源集团 [21, 53]红外图像识别技术 中国矿业大学[54-55]
中国矿业大学（北京）[56]
辽宁工程技术大学[41]
河南理工大学[8]
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