煤矿掘进多行为协同控制智能决策模型

王宏伟; 郄晨飞; 付翔; 李进; 王浩然

doi:10.13272/j.issn.1671-251x.18114

煤矿掘进多行为协同控制智能决策模型

doi: 10.13272/j.issn.1671-251x.18114

王宏伟^{1, 2, 4,},
郄晨飞^{1, 2, ,},
付翔^{1, 2},
李进^{1, 4},
王浩然^{1, 3}

1.
太原理工大学山西省煤矿智能装备工程研究中心, 山西太原　030024
2.
太原理工大学矿业工程学院, 山西太原　030024
3.
太原理工大学安全与应急管理工程学院, 山西太原　030024
4.
太原理工大学机械与运载工程学院, 山西太原　030024

基金项目: 国家重点研发计划项目（2020YFB1314004）；山西省揭榜招标项目(20201101008)；山西省重点研发计划项目(202102100401015)。

详细信息

作者简介:
王宏伟(1977—)，女，黑龙江勃利人，教授，博士，博士研究生导师，主要研究方向为煤机装备智能化、人工智能与5G+智慧矿山等，E-mail：lntuwhw@126.com

通讯作者:
郄晨飞(1997—)，男，山西忻州人，硕士研究生，研究方向为煤矿智能化、协同控制，E-mail：1217297735@qq.com

中图分类号: TD632
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出版历程
- 收稿日期: 2023-05-08
- 修回日期: 2023-06-15
- 网络出版日期: 2023-07-10

Intelligent decision-making model of multi-behavior collaborative control in coal mine excavation

WANG Hongwei^{1, 2, 4
,},
QIE Chenfei^{1, 2
, ,},
FU Xiang^{1, 2},
LI Jin^{1, 4},
WANG Haoran^{1, 3}

1.
Center of Shanxi Engineering Research for Coal Mine Intelligent Equipment, Taiyuan University of Technology, Taiyuan 030024, China
2.
College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
3.
College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan 030024, China
4.
College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China

摘要

摘要: 智能决策支持的掘进多行为协同控制是煤矿掘进工作面智能化的核心之一，掘进多行为协同控制的最优时序规划是智能决策的关键。针对煤矿掘进多行为控制模式单一、固化、协同作业能力差等问题，设计了一种煤矿掘进多行为协同控制智能决策模型，实现了掘进多行为在最优时序下的协同作业。首先，提出了掘进多行为协同控制智能决策方法，确定了掘进多行为可行时序规划集和多目标最优时序规划策略；其次，根据掘进现场的规定和工艺要求，确定了掘进动作事件集，通过对事件集中两两动作事件之间时间关系的分析，求出掘进多行为时间关系约束矩阵；然后，根据时间点关系约束矩阵转换方法，将掘进多行为时间关系约束矩阵转换为时间点关系约束矩阵，再求出掘进多行为可行时序规划集；最后，定义不同掘进目标下的求解函数，求得不同掘进目标的最优时序。实验结果表明，在不同掘进目标下，按照模型决策出的掘进动作最优时序规划结果，掘进机器人可无干涉协同作业，且掘进作业1个工作循环的执行时间与决策模型计算的时间基本一致。
- 掘进工作面 /
- 协同作业 /
- 多行为协同控制 /
- 智能决策 /
- 最优时序规划 /
- 掘进动作事件集
Abstract: Intelligent decision-making support for multi-behavior collaborative control in coal mine excavation is one of the core functions of the coal mine excavation working face. The optimal time series planning of multi-behavior collaborative control in excavation is the key to intelligent decision-making. In order to solve the problems of single control mode, solidification and poor collaborative operation capability of multi-behavior in coal mine excavation, an intelligent decision-making model of multi-behavior collaborative control in coal mine excavation is designed. It realizes the collaborative operation of multi-behavior in the optimal time series. Firstly, an intelligent decision-making method for excavation multi-behavior collaborative control is proposed. The feasible time series planning set and multi-objective optimal time series planning strategy for excavation multi-behavior are determined. Secondly, based on the regulations and process requirements of the excavation site, a set of excavation action events is determined. By analyzing the time relationship between two action events in the event set, a constraint matrix for the time relationship of excavation multi-behaviors is obtained. Thirdly, based on the transformation method of the time relationship constraint matrix, the multi-behavior time relationship constraint matrix of excavation is transformed into a time relationship constraint matrix. The feasible time series planning set of excavation multi-behavior is obtained. Finally, the solving functions for different excavation objectives are defined and the optimal time series for different excavation objectives is obtained. The experimental results show that the excavation robot can work collaboratively without interference according to the optimal time series planning results of the excavation action determined by the model under different excavation objects. The execution time of one working cycle of the excavation operation is basically consistent with the time calculated by the decision-making model.
- excavation working face /
- collaborative work /
- multi-behavior collaborative control /
- intelligent decision-making /
- optimal time series planning /
- excavation action event set

HTML全文

图 1 掘进多行为协同控制智能决策方法流程

Figure 1. Process of intelligent decision-making method for multi-behavior collaborative control in excavation

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图 2 某回风巷支护工艺

Figure 2. Support technology for a return air roadway

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图 3 掘进多行为最优时序计算流程

Figure 3. The optimal time series calculation process of multi-behavior in excavation

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图 4 实验设备及部分掘进动作事件标注

Figure 4. Experimental equipment and partial excavation action event labeling

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图 5 掘进多行为协同控制实验结果

Figure 5. Experimental results of multi-behavior collaborative control in excavation

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表 1 掘进动作事件集

Table 1. Excavation action event set

事件	动作类型
B₁	掘锚一体机截割煤壁
B₂	掘锚一体机左顶钻臂左上顶板第1根钻锚
B₃	掘锚一体机右顶钻臂右上顶板第1根钻锚
B₄	掘锚一体机左顶钻臂左上顶板第2根钻锚
B₅	掘锚一体机右顶钻臂右上顶板第2根钻锚
B₆	掘锚一体机左帮钻臂左帮第1根钻锚
B₇	掘锚一体机右帮钻臂右帮第1根钻锚
B₈	掘锚一体机左帮钻臂左帮第2根钻锚
B₉	掘锚一体机右帮钻臂右帮第2根钻锚
B₁₀	锚杆转载机左帮钻臂左帮第3根钻锚
B₁₁	锚杆转载机右帮钻臂右帮第3根钻锚
B₁₂	锚杆转载机左帮钻臂左帮第4根钻锚
B₁₃	锚杆转载机右帮钻臂右帮第4根钻锚
B₁₄	掘锚一体机锚索钻臂顶板第1根锚索钻锚
B₁₅	掘锚一体机锚索钻臂顶板第2根锚索钻锚
B₁₆	掘锚一体机铺设锚网到临时支撑上
B₁₇	掘锚一体机铲板/锚杆转载机前支撑上升
B₁₈	掘锚一体机铲板/锚杆转载机前支撑下降
B₁₉	“两机”后支撑上升
B₂₀	“两机”后支撑下降
B₂₁	掘锚一体机临时支撑升起
B₂₂	“两机”向前行走
B₂₃	掘锚一体机铺设帮锚网
B₂₄	掘锚一体机临时支撑收回

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表 2 13种时间元关系表示

Table 2. Representations of 13 time element relationships

两两行为之间13种时间关系表示	符号	时间点逻辑法表示	图形表示（B_i： B_j：）
Before	<	a_i＜ b_i＜a_j＜b_j
Meet	m	a_i＜ b_i=a_j＜b_j
Overlap	0	a_i＜ a_j ＜ b_i ＜b_j
Finished By	fi	a_i＜ a_j ＜ b_i =b_j
Contains	di	a_i ＜ a_j ＜ b_j ＜ b_i
Start	s	a_i = a_j ＜ b_i ＜b_j
Equal	=	a_i = a_j ＜ b_i =b_j
Start By	si	a_i = a_j ＜ b_j ＜ b_i
During	d	a_j ＜ a_i ＜ b_i ＜b_j
Finish	f	a_j ＜ a_i ＜ b_i =b_j
Overlaped By	oi	a_j ＜ a_i＜ b_j ＜ b_i
Meet By	mi	a_j ＜ b_j =a_i＜ b_i
After	>	a_j ＜ b_j ＜a_i＜ b_i

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表 3 模型求解目标及对应数学函数

Table 3. Model solving objectives and corresponding mathematical functions

求解目标	数学函数
掘进作业最短时间	t_min=min(f(R_k))，f(R_k)为R_k的执行时间
掘进作业最接近目标时间	t={f(R_k)\|min(\|f(R_k) − S\|)}，S为目标时间
掘进作业最长时间	t_max=max(f(R_k))

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表 4 部分掘进多行为可行时序集

Table 4. Feasible timing sets of partial excavation multi-behavior

序号	掘进动作时序
1	a₂₂ [a₁₆ a₂₀ b₂₂] b₂₀ [b₁₆ a₂₁] a₁₈ [a₁ a₂ a₃ a₁₄ a₁₅ b₁₈ b₂₁] [b₂ a₄] [b₃ a₅] [b₁ a₂₃] b₄ b₅ [a₁₀ a₁₁] [b₁₀ a₁₂] [b₁₁ a₁₃] b₁₂ b₁₃ a₂₄ b₂₄ [a₆ a₇ b₂₃] b₁₄ b₁₅ [b₆ a₈] [b₇ a₉] b₈ b₉ [a₁₇ a₁₉] b₁₇ b₁₉
2	a₂₂ [b₂₂ a₁₆] b₁₆ a₁₈ b₁₈ a₂₀ b₂₀ a₁₀ b₁₀ a₁₁ b₁₁ a₁₂ b₁₂ a₁₃ b₁₃ a₁₄ b₁₄ a₁₅ a₂₁ [b₂₁ a₁]b₁ a₂ b₂ a₃ b₃ b₁₅ a₄ b₄ a₅ b₅ a₂₃ [b₂₃ a₆] b₆ a₇ b₇ a₈ b₈ a₉ b₉ a₁₇ b₁₇ a₁₉ b₁₉ a₂₄ b₂₄
3	a₂₂ [a₁₆ a₂₀ b₂₂] b₂₀ [b₁₆ a₂₁] a₁₈ [a₁ a₂ a₃ a₁₄ a₁₅ b₁₈ b₂₁] [b₂ a₄] [b₃ a₅] [b₁ a₂₃] b₄ b₅ [a₁₀ a₁₁] [b₁₀ a₁₂] [b₁₁ a₁₃] b₁₂ b₁₃ a₂₄ b₂₄ [a₆ a₇ b₂₃] b₁₄ b₁₅ [b₆ a₈] [b₇ a₉] [b₈ b₉ a₁₇ a₁₉] b₁₇ b₁₉
4	a₂₂ [a₁₆ a₂₀ b₂₂] b₂₀ [b₁₆ a₂₁] a₁₈ [a₁ a₂ a₃ a₁₄ a₁₅ b₁₈ b₂₁] [b₂ a₄] [b₃ a₅] [b₁ a₂₃] b₄ b₅ [a₁₀ a₁₁] [b₁₀ a₁₂] [b₁₁ a₁₃] b₁₂ b₁₃ a₂₄ b₂₄ [a₆ a₇ b₂₃] b₁₅ b₁₄ [b₆ a₈] [b₇ a₉] b₉ b₈ [a₁₇ a₁₉] b₁₇ b₁₉
5	a₂₂ [a₁₆ a₂₀ b₂₂] b₂₀ [b₁₆ a₂₁] a₁₈ [a₁ a₂ a₃ a₁₄ a₁₅ b₁₈ b₂₁] [b₂ a₄] [b₃ a₅] [b₁ a₂₃] b₄ b₅ [a₁₀ a₁₁] [b₁₀ a₁₂] [b₁₁ a₁₃] b₁₂ b₁₃ a₂₄ b₂₄ [a₆ a₇ b₂₃] b₁₅ b₁₄ [b₇ a₉] [b₆ a₈] b₉ [b₈ a₁₇ a₁₉] b₁₉ b₁₇
6	a₂₂ b₂₂ a₁₆ b₁₆ a₁₈ b₁₈ a₂₀ b₂₀ a₁₀ b₁₀ a₁₁ b₁₁ a₁₂ b₁₂ a₁₃ b₁₃ a₁₄ b₁₄ a₁₅ b₁₅ a₂₁ b₂₁ a₁ b₁ a₂ b₂ a₃ b₃ a₄ b₄ a₅ b₅ a₂₃ b₂₃ a₆ b₆ a₇ b₇ a₈ b₈ a₉ b₉ a₁₇ b₁₇ a₁₉ b₁₉ a₂₄ b₂₄
7	a₂₂ [a₁₆ a₂₀ b₂₂] b₂₀ [b₁₆ a₂₁] a₁₈ [a₁ a₂ a₃ a₁₀ a₁₁ a₁₄ a₁₅ b₁₈] b₂₁ [b₂ a₄ b₃ a₅] [b₁ b₄ b₅] [b₁₀ a₁₂ b₁₁] a₁₃ [b₁₂ b₁₃] [ a₂₃ a₂₄] b₂₄ [a₆ a₇ b₂₃] [b₁₅ b₁₄] [b₆ a₈ b₇ a₉] [b₉ b₈] [ a₁₇ a₁₉] b₁₇ b₁₉
8	a₂₂ [a₁₆ a₂₀ b₂₂] b₂₀ [b₁₆ a₂₁] a₁₈ [a₁ a₂ a₃ a₁₀ a₁₁ a₁₄ a₁₅ b₁₈] b₂₁ [b₂ a₄ b₃ a₅] [b₁ b₄ b₅] [b₁₀ a₁₂ b₁₁ a₁₃] [b₁₂ b₁₃] [a₂₃ a₂₄] b₂₄ [a₆ a₇ b₂₃] [b₁₅ b₁₄] [b₆ a₈ b₇ a₉] [b₉ b₈ a₁₇ a₁₉] [b₁₇ b₁₉]

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表 5 掘进动作事件时间

Table 5. Excavation action event time

事件	时间/min	事件	时间/min	事件	时间/min
B₁	7.0	B₉	3.0	B₁₇	0.3
B₂	3.0	B₁₀	3.0	B₁₈	0.3
B₃	3.0	B₁₁	3.0	B₁₉	0.3
B₄	3.0	B₁₂	3.0	B₂₀	0.3
B₅	3.0	B₁₃	3.0	B₂₁	0.5
B₆	3.0	B₁₄	15.0	B₂₂	1.0
B₇	3.0	B₁₅	15.0	B₂₃	1.0
B₈	3.0	B₁₆	1.0	B₂₄	0.5

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表 6 掘进多行为最优时序模型实例化结果

Table 6. Instantiation results of optimal time series model for multi-behavior in excavation

求解目标	掘进动作时间点序	时间/min
掘进作业最短时间	a₂₂ [a₂₀ b₂₂ a₁₆ a₁₈][b₂₀ b₁₈ a₁₄ a₁₅ a₁₀ a₁₁][b₁₆ a₂₁] [b₂₁ a₁ a₂ a₃] [b₁₀ b₁₁ a₁₃ a₁₂] [b₂ b₃ a₄ a₅] [b₁₂ b₁₃] [b₄ b₅][b₁ a₂₃ a₂₄] b₂₄[b₂₃ a₆ a₇][b₆ b₇ a₈ a₉] [b₁₄ b₁₅] [b₈ b₉ a₁₇ a₁₉] [b₁₉ b₁₇]	17
以32 min为掘进作业目标时间	a₂₂ [a₂₀ b₂₂ a₁₆ a₁₈][b₂₀ b₁₈ a₁₀ a₁₁][b₁₆ a₂₁] [b₂₁ a₁ a₂ a₃] [b₁₀ b₁₁ a₁₃ a₁₂] [b₂ b₃ a₄ a₅] [b₁₂ b₁₃] [b₄ b₅] [b₁ a₂₃ a₂₄] b₂₄[b₂₃ a₆ a₇][b₆ b₇ a₈ a₉] [b₈ b₉ a₁₄ a₁₅][ b₁₄ b₁₅ a₁₇ a₁₉] [b₁₉ b₁₇]	32
掘进作业最长时间	a₂₂ b₂₂ a₁₆ b₁₆ a₁₈ b₁₈ a₂₀ b₂₀ a₁₀ b₁₀ a₁₁ b₁₁ a₁₂ b₁₂ a₁₃ b₁₃ a₁₄ b₁₄ a₁₅ b₁₅ a₂₁ b₂₁ a₁ b₁ a₂ b₂ a₃ b₃ a₄ b₄ a₅ b₅ a₂₃ b₂₃ a₆ b₆ a₇ b₇ a₈ b₈ a₉ b₉ a₁₇ b₁₇ a₁₉ b₁₉ a₂₄ b₂₄	79

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煤矿掘进多行为协同控制智能决策模型

doi: 10.13272/j.issn.1671-251x.18114

作者简介:
王宏伟(1977—)，女，黑龙江勃利人，教授，博士，博士研究生导师，主要研究方向为煤机装备智能化、人工智能与5G+智慧矿山等，E-mail：lntuwhw@126.com

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郄晨飞(1997—)，男，山西忻州人，硕士研究生，研究方向为煤矿智能化、协同控制，E-mail：1217297735@qq.com

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Intelligent decision-making model of multi-behavior collaborative control in coal mine excavation

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煤矿掘进多行为协同控制智能决策模型

doi: 10.13272/j.issn.1671-251x.18114

作者简介: 王宏伟(1977—)，女，黑龙江勃利人，教授，博士，博士研究生导师，主要研究方向为煤机装备智能化、人工智能与5G+智慧矿山等，E-mail：lntuwhw@126.com

通讯作者: 郄晨飞(1997—)，男，山西忻州人，硕士研究生，研究方向为煤矿智能化、协同控制，E-mail：1217297735@qq.com

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Intelligent decision-making model of multi-behavior collaborative control in coal mine excavation

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作者简介:
王宏伟(1977—)，女，黑龙江勃利人，教授，博士，博士研究生导师，主要研究方向为煤机装备智能化、人工智能与5G+智慧矿山等，E-mail：lntuwhw@126.com

通讯作者:
郄晨飞(1997—)，男，山西忻州人，硕士研究生，研究方向为煤矿智能化、协同控制，E-mail：1217297735@qq.com