1492 Figure 7 - Attitude change of supports in steeply dipping working face 4. CONCLUSIONS The digital twin-based attitude perception and simulation system for hydraulic supports can monitor real-time attitude changes under steeply dipping conditions and provide early warnings upon support tipping, significantly enhancing the capability for state perception and abnormal response. The experiments revealed the dynamic load transfer law under the dip angle effect: the average load on the canopy under impact loads (3.75 kN under frontal impact) exceeded that under static load (average 3.63 kN). The load on the side guard of the canopy increased periodically under side-push impact. And under rear-push impact, the load on the shield beam was highest in the upper incline section, followed by the middle, and smallest in the lower section. Support instability and tipping can be triggered when loading leads to roof rupture, indicating the necessity for enhanced anti-overturning and anti-sliding control in steeply dipping faces. Digital twin technology provides crucial theoretical support and an experimental verification means for the dynamic stability control of supports. ACKNOWLEDGEMENTS We thank all authors for their contributions to this study. REFERENCES Xie Heping, Wang Jinhua, Wang Guofa, et al. New ideas of coal revolution and layout of coal science and technology development[J]. Journal of China Coal Society, 2018,43(5).1187-1197. Wang Guofa. New technological progress of coal mine intelligence and its problems[J]. Coal Science and Technology,2022,50(1).1-27. Liu Feng, Cao Wenjun, Zhang Jianming, et al. Current technological innovation and development direction of the 14th Five-Year Plan period in China coal industry[J]. Journal of China Coal Society,2021,46(01).1-15. Wang Guofa. Development Orientation of Complete Fully-Mechanized Automation, Intelligent and Unmanned Mining Technology and Equipment[J]. Coal Science and Technology,2014,42(09).3034. Fan Liuqun, Ding Kai, Liu Guangjie. Digital twin technology within intelligent manufacturing[J]. Manufacturing Technology and Machine Tools,2019,07.61-66.
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