Intelligent assessment method for rockburst hazard areas based on image recognition technology
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摘要:
针对传统冲击地压危险评价方法计算量大、危险区域划分精度低等问题，为适应冲击地压防治智能化、可视化的发展需求，提出了一种基于图像识别技术的冲击地压危险区域智能化评价方法。采用半定量化估算方法，对11项冲击地压危险的动静载主控因素进行量化表征；基于OpenCV机器视觉库和深度学习模型，实现对单一主控因素的图像识别；通过构建图像灰阶与应力集中系数的映射矩阵，实现对单一影响因素的线性与非线性叠加，得到评价区域的应力集中系数矩阵；采用min−max标准化法构建冲击地压危险区域的“无、弱、中等、强”4级判别标准，实现分级分区评价。基于Python语言开发了冲击地压危险智能化评价软件，并对软件实际应用效果进行了检验，结果表明：软件将传统仅针对巷道的一维线性危险区域划分方法改进为针对整个采掘空间的二维平面划分方法，显著提高了评价效率和危险区域划分精度，降低了人工成本；评价结果与微震能量密度云图、现场实测矿压规律一致性较高，可为现场冲击地压防治工作提供有效指导。
Abstract:In traditional rockburst hazard assessment methods, there are problems of large computational complexity and low precision in dividing hazardous areas. In order to meet the development needs of intelligent and visual prevention and control of rockburst, an intelligent assessment method for rockburst hazard areas based on image recognition technology is proposed. Using a semi quantitative estimation method, the method quantitatively characterizes the main controlling factors of dynamic and static loads for 11 types of rockburst hazards. Based on OpenCV machine vision library and deep learning model, the method achieves image recognition for a single main control factor. By constructing a mapping matrix between the grayscale of the image and the stress concentration coefficient, linear and nonlinear superposition of a single influencing factor is achieved to obtain the stress concentration coefficient matrix of the assessment area. Using the min max standardization method to construct a 4-level discrimination standard of "no, weak, moderate, and strong" for the hazard area of rockburst, the method achieves graded and division assessment. A software for intelligent assessment of rockburst hazards is developed based on Python language, and the actual application effect of the software is tested. The results show that the software improves the traditional one-dimensional linear hazard area division method for roadways to a two-dimensional plane division method for the entire mining space. It significantly improvies the assessment efficiency and precision of hazard area division and reduces labor costs. The assessment results are highly consistent with the microseismic energy density cloud map and the on-site measured mining pressure pattern, which can provide effective guidance for the prevention and control of on-site rockburst.
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Key words:
- coal mining /
- rockburst /
- hazard assessment /
- hazard area division /
- image recognition
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图 1 冲击地压危险主控因素及影响范围
Figure 1. Main controlling factors and influence range of rock burst hazard
图 3 工作面回采接近断层时煤体应力集中系数变化曲线
Figure 3. Change curve of stress concentration coefficient of coal body when mining face approaches fault
图 4 采空区形成后煤体应力集中系数变化曲线
Figure 4. Change curve of stress concentration coefficient of coal body after the formation of goaf
图 5 不同区段煤柱宽度下煤体应力集中系数变化曲线
Figure 5. Change curve of stress concentration coefficient of coal body under different coal pillar widths
图 6 终采线外错区域煤体垂直应力分布
Figure 6. Vertical stress distribution of coal body in staggered area outside terminal line
图 7 终采线外错区域煤体应力集中系数变化曲线
Figure 7. Change curve of stress concentration coefficient in staggered area outside terminal line
图 9 “T”型和“X”型交叉巷道区域数值模拟结果
Figure 9. Numerical simulation results of "T" and "X" shaped cross-roadway
图 10 21104工作面冲击地压危险主控因素的量化表征与图形识别
Figure 10. Quantitative characterization and graphic identification of main controlling factors of rock burst hazard in 21104 working face
图 13 冲击地压危险智能化评价软件界面
Figure 13. Interface of intelligent evaluation software of rock burst hazard
图 14 冲击地压危险性评价结果与微震能量密度云图对比
Figure 14. Comparison between rock burst hazard assessment results and cloud picture of microseismic energy density
图 15 21104工作面冲击地压危险预警及矿压异常区域分布
Figure 15. Distribution of rock burst hazard early warning area and abnormal area of mine pressure in 21104 working face
表 1 冲击地压危险主控因素的量化表征结果
Table 1. Quantitative characterization results of main controlling factors of rock burst hazard
序号 影响因素 识别主体 影响范围 应力集中系数 1 采深 采深 （400，600] m 1.20 （600，800] m 1.30 （800，1 000] m 1.40 （1 000，+∞） m 1.60 2 褶曲 倾角≤15°褶曲 褶曲轴部前后10 m 1.20 倾角＞15°褶曲 褶曲轴部前后20 m 1.40 3 断层 落差＞10 m断层 断层前后30～50 m 1.40 断层前后10～30 m 1.90 断层前后<10 m 2.05 落差≤10 m断层 断层前后30 m 1.20 4 采空区 采空区 采空区外缘50 m 1.50 5 区段煤柱 区段煤柱宽度 [50，+∞） m或（－∞，5] m 1.00 （5，10] m 1.30 （10，30] m 2.20 （30，50] m 1.50 6 开切眼外错 开切眼外错拐点 外错点前后30～50 m 1.50 外错点前后30 m 2.10 7 终采线外错 终采线外错拐点 外错点前后30～50 m 1.50 外错点前后30 m 2.10 8 巷道交叉 “T”型交叉 “T”型交叉前后30 m 1.05 “X”型交叉 “X”型交叉前后30 m 1.10 9 初次来压 初次来压线 初次来压前后20 m 1.70 10 “见方”破断 初次“见方”线 初次“见方”前后50 m 1.50 二次“见方”线 二次“见方”前后50 m 1.70 11 矿震动载 矿震平均能量 （103～104] J的工作面 1.10 （104～105] J的工作面 1.20 （105，+∞） J的工作面 1.60 
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表 2 冲击地压危险区域的划分标准
Table 2. Classification standard of rock burst hazard area
冲击地压
危险等级应力集中系数 灰阶 无 $ [0,{\lambda _{\min }}) $ $ [0,k{\lambda _{\min }}) $ 弱 $ [{\lambda _{\min }},\left({{{\lambda _{\max }} - {\lambda _{\min }}}}\right)/{3}) $ $ [k{\lambda _{\min }},{{k({\lambda _{\max }} - {\lambda _{\min }})}}/{3}) $ 中等 $ \left[\dfrac{{{\lambda _{\max }} - {\lambda _{\min }}}}{3},\dfrac{{2({\lambda _{\max }} - {\lambda _{\min }})}}{3}\right) $ $ \left[\dfrac{{k({\lambda _{\max }} - {\lambda _{\min }})}}{3},\dfrac{{2k({\lambda _{\max }} - {\lambda _{\min }})}}{3}\right) $ 强 $ [{{2({\lambda _{\max }} - {\lambda _{\min }})}}/{3}, + \infty ) $ $ [{{2k({\lambda _{\max }} - {\lambda _{\min }})}}/{3}, + \infty ) $
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