Study on Automatic Top Coal Caving System in Fully-Mechanized Coal Caving Face
More Information
Available Online:
April 02, 2023
Published Date:
November 24, 2013
Abstract
In order to meet safety and high efficiency, realize automate drawing, optimal scheme of caving coal was analyzed under the condition of different caving ratios by computer simulation. Applying the Industrial Ethernet as basic communication technologies, 1 000 MB optical fiber ring network technology was applied to the entire face. Some stationary equipments such as support controller, conveyor, reversed loader, and crushers were switched on network by the method of wired. For mo bile equipment such as shearer which was not convenient to be wired, access the network using wireless technology were switched on network to realize intelligent con trol communication in working face. It was firstly introduced that intelligent attitude control equipment coupled, bracket mounted caving intelligent attitude control equip ment coupled, caving agency attitude contral equipment and top-coal caving memory equipment was installed in top beam web horizontal section, at the end of the bea m web and inside the controller of hydraulic support. Translates to perform actions to automatic caving was realized by the detection, matching and transmission of corr elation signal.
Related Articles
[1] CUI Yazhong, HE Jianrong, REN Yanyan. Research on application of artificial intelligence safety production management and control platform in Shendong mining area [J]. COAL SCIENCE AND TECHNOLOGY, 2025, 53(S1): 275-283. DOI: 10.12438/cst.2024-0102
[2] LEI Zhiyong, MA Xiaolong, ZHAO Shujun, ZHANG Shiming, YAN Bin. Intelligent collaborative management and control platform for continuous mining equipment in open-pit mines [J]. COAL SCIENCE AND TECHNOLOGY, 2025, 53(4): 362-372. DOI: 10.12438/cst.2024-1525
[3] LI Donghui, LI Dongyin, WANG Shen, HUANG Zhizeng, LIU Qing, ZHANG Xueliang, ZHENG Lijun, ZHANG Xuhe, ZHU Shiting. Safe passing critical criterion for drawn top-coal on rear conveyor and accurate control approach for drawing opening dimension [J]. COAL SCIENCE AND TECHNOLOGY, 2023, 51(9): 251-260. DOI: 10.13199/j.cnki.cst.2022-1010
[4] CUI Yao, YE Zhuang. Research on cloud-edge-terminal collaborative intelligent control of coal shearer based on 5G communication [J]. COAL SCIENCE AND TECHNOLOGY, 2023, 51(6): 205-216. DOI: 10.13199/j.cnki.cst.2022-1017
[5] LI Shuang, HE Chao, LU Cheng, XU Kun, XUE Guangzhe. Research on intelligent dual prevention mechanism and intelligent security control platform of coal mine [J]. COAL SCIENCE AND TECHNOLOGY, 2023, 51(1): 464-473. DOI: 10.13199/j.cnki.cst.2022-2155
[6] ZHANG Xiaoxia, CHEN Siyu, SU Shanghai, WANG Haili. Design and application of mine intelligent integrated management and control platform [J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(9): 168-178.
[7] BAI Yongsheng, NIU Jianfeng. Design of transparency of communication network system for electronic-hydraulic control system of hydraulic support [J]. COAL SCIENCE AND TECHNOLOGY, 2018, (9).
[8] Huang Zenghua Nan Bingfei Zhang Kexue Feng Yinhui, . Design on intelligent control platform of mechanized mining robot based on Ethernet/IP [J]. COAL SCIENCE AND TECHNOLOGY, 2017, (5).
[9] QIU Jin-bo. Development and Application of Shearer Automation and Intelligent Control Technology [J]. COAL SCIENCE AND TECHNOLOGY, 2013, (11).
[10] Study on Mining and Caving Ratio of Fully Mechanized Top Coal Caving Mining Face Affected to Top Coal Recovery Rate [J]. COAL SCIENCE AND TECHNOLOGY, 2013, (3).
Cited by
Periodical cited type(18)
1.
李阳. 影视拍摄机器人中基于目标检测信息的跟踪算法应用研究. 自动化与仪器仪表. 2025(06)
2.
毛清华,苏毅楠,贺高峰,翟姣,王荣泉,尚新芒. 基于改进YOLOv8模型的井下人员入侵带式输送机危险区域智能识别. 工矿自动化. 2025(01): 11-20+103 .
3.
李小军,赵明炀,李淼. 基于深度学习的钻孔冲煤量智能识别方法. 煤田地质与勘探. 2025(01): 257-270 .
4.
路洋,董立红,叶鸥. 基于自适应链接优化的井下行人抗遮挡跟踪方法研究. 工矿自动化. 2025(02): 65-75+137 .
5.
柳小波,范立鹏,秦丽杰,王连成,张兴帆. 机器视觉技术在矿山行业的应用现状与展望. 有色金属(矿山部分). 2025(02): 1-15 .
6.
唐俊飞,邢海龙,李溯,张涛涛,刘恒,姚诗雨. 结合改进CNN与自注意力机制的煤矿轮式机器人目标检测技术. 煤矿安全. 2025(03): 224-232 .
7.
毛清华,翟姣,胡鑫,苏毅楠,薛旭升. 煤矿综采工作面人员入侵危险区域智能识别方法. 煤炭学报. 2025(02): 1347-1361 .
8.
井晶,高宇蒙,赵作鹏,闵冰冰. 一种改进的Yolov5s煤矿井下人员计数模型. 计算机仿真. 2025(04): 525-530+551 .
9.
张宇豪,肖新宇,朱梓润,訾梦超,廖金湘. 一种基于DeepSORT的逆透视车流量检测方法. 轻工科技. 2024(01): 98-100 .
10.
王洪磊,郭鑫,张亦凡,张俊升. 煤质煤量全面在线检测技术发展现状及应用进展. 煤炭科学技术. 2024(02): 219-237 .
本站查看
11.
邵小强,李鑫,杨永德,原泽文,杨涛. 基于改进YOLOv7的矿井人员检测算法. 电子科技大学学报. 2024(03): 414-423 .
12.
王茂森,鲍久圣,章全利,杨阳,袁晓明,阴妍,张可琨,葛世荣. 煤矿井下单轨吊无人驾驶目标识别算法与轨道接缝检测方法. 煤炭学报. 2024(S1): 457-471 .
13.
狄靖尧,杨超宇. 基于改进Transformer的井下人员检测算法. 科学技术与工程. 2024(26): 11188-11194 .
14.
解北京,李恒,董航,栾铮,张奔,李晓旭. 基于多尺度特征融合井下猴车载人状态的智能识别算法与应用. 煤炭科学技术. 2024(12): 272-286 .
本站查看
15.
孙林,陈圣,姚旭龙,张艳博,陶志刚,梁鹏. 煤矿井下残缺信息的多目标检测方法研究. 煤炭科学技术. 2024(S2): 211-220 .
本站查看
16.
范伟强,王雪瑾,张颖慧,李晓宇. 改进YOLOv7和DeepSORT的井下人员检测与跟踪算法. 煤炭科学技术. 2024(S2): 343-355 .
本站查看
17.
程德强,寇旗旗,江鹤,徐飞翔,宋天舒,王晓艺,钱建生. 全矿井智能视频分析关键技术综述. 工矿自动化. 2023(11): 1-21 .
18.
郝明月,闵冰冰,张新建,赵作鹏,吴晨,王欣. 基于改进YOLOv5s的矿工排队检测方法. 工矿自动化. 2023(11): 160-166 .
Other cited types(12)