煤矿智能化是煤炭工业高质量发展的核心技术支撑，是煤炭工业技术革命和升级发展的必然方向。综合机械化采煤（以下简称综采）智能化是煤矿智能化的关键技术之一，它的实现将会极大促进煤矿智能化的发展，有力提升煤炭的安全、高效、绿色开采水平。综采设备主要包括液压支架、采煤机、刮板输送机、转载机、破碎机和设备列车等，其中综采工作面多设备—液压支架、采煤机和刮板输送机（简称综采“三机”）相互配套、各自分工、协同作业，共同承担着综采工作面的支护、破煤和输煤任务。因此，实现综采工作面多设备的协同控制是实现综采工作面智能化的关键。
      本文依托国家重点研发计划项目“煤矿智能开采安全技术与装备研发”子课题“大采高工作面多设备协同作业控制机制研究”及国家自然科学基金项目（编号：2017YFC0804310、51805428），以综采工作面多设备协同控制的决策方法为研究主线，重点解决综采工作面多设备状态数据预处理问题，综采工作面多设备协同控制决策策略学习问题和综采工作面多设备协同控制的智能决策问题，通过实验室实验和现场数据对综采工作面多设备协同控制的决策模型进行验证，具体内容如下：
      （1）针对智能综采工作面工作面多设备协同控制的决策模型缺乏问题，深入分析综采工作面多设备协同作业关系及其人工操作控制过程，确定综采工作面多设备协同控制基本思路，建立综采工作面多设备协同控制系统模型与架构，给出综采工作面多设备协同控制决策模型的关键问题与解决方案，为后续研究奠定基础。
      （2）针对综采工作面多设备状态数据存在噪声污染和特征参数冗余的问题，设计基于动态融合局部异常因子与堆叠去噪自编码器的数据清洗方法（DFLOF-SDAE），提升数据质量；基于综采工作面多设备协同作业的粗糙有限状态机模型，建立综采工作面多设备协同控制多源状态数据关联决策表；提出多标记自适应模糊邻域粗糙集及其特征参数选择算法（MLAFNRS），对多数据类型的综采工作面多设备决策数据表进行特征参数选择，最后采用对比实验验证提出方法的准确性。
      （3）针对非线性、强耦合、时空关联数据的综采工作面多设备协同控制决策策略学习问题，首先提出一种新的基于宽度架构的模糊粗糙宽度神经网络（FRBNN），实现强耦合非线性综采工作面多设备监测数据隐性特征的提取；然后以模糊粗糙宽度神经网络为核心，构建综采工作面多设备协同控制的决策策略学习方法（SL-FRBNN），通过获得输入参数本身的时序特征信息和多输入参数间的空间关联特征信息，实现综采工作面多设备协同控制决策策略的学习，最后通过综采工作面多设备协同控制实验台对SL-FRBNN方法进行验证，结果表明：与ELM、FRNN、BLS、LSTM算法相比，SL-FRBNN方法在综采工作面多设备行为决策、采煤机滚筒调高以及刮板输送机与采煤机协同调速中均具有更高的精度和可靠性。
      （4）针对动态不稳定工况下的综采工作面多设备协同控制决策问题，提出基于增量式选择性集成的综采工作面多设备协同控制决策方法（ISEDFM-FRBNN&KELM）。该模型基于集成决策思想，首先采用Bootstrap重采样方法生成多个训练集，并用该训练集构建多个具有自学习能力的二元混合异样FRBNN&KELM基决策器，然后采用双错测度选择性集成方法对基决策器进行选择性集成，用于综采工作面多设备协同作业过程的在线决策，最后通过综采工作面多设备协同控制实验台对ISEDFM-FRBNN&KELM方法进行验证，结果表明：提出的ISEDFM-FRBNN&KELM集成决策方法能有效实现综采工作面多设备的行为决策、采煤机滚筒的智能调高以及刮板输送机负载变化下采煤机的自适应调速。
      （5）为进一步验证本文提出的综采工作面多设备协同控制决策模型的合理性和可行性，采用榆家梁煤矿43101综采工作面设备运行数据对算法进行验证，结果表明：本文提出的综采工作面多设备协同控制决策方法合理可行，通过应用提出的特征参数选择算法、协同控制决策策略学习算法和动态选择性集成决策方法，能有效实现综采工作面多设备的自主协同作业。
       本文提出的综采工作面多设备协同控制的智能决策方法，为综采工作面关键设备的自主协同作业提供了一条新的解决途径，为综采工作面智能化发展奠定了基础。
 

~The intellectualization of coal mine is core technological support for high-quality development of coal industry and is Inevitable direction for technological revolution and upgrading of coal industry. The intellectualization of comprehensive mechanized coal mining (referred to as comprehensive mining) is one of the key technologies in intellectualization of coal mine, and its realization will greatly promote the development of intelligent coal mine improving the level of safe, efficient and green mining powerfully. The equipment for comprehensive mining mainly includes hydraulic support, coal shearer, scraper conveyor, transfer machine, crusher and equipment train,etc., and hydraulic support, Shearer, and scraper conveyor in them can support each other, work divided and operate synergetic, which are responsible for the task of support, destroying and transporting coal on comprehensive mining face supporting each other, their respective division of labor, collaborative work. Therefore, realizing cooperative control to "three machines" during comprehensive mining is the key to realize intellectualization on comprehensive mechanized coal mining face.
This dissertation, which relies on sub-project "study on control mechanism of multi-equipment cooperative operation in large mining height working face" (Grant No. 2017YFC0804310) of National Key Research and Development Program "safety technology and equipment research and development of intelligent coal mining", takes the three fully mechanized coal mining machines as the research object and the cooperative control decision-making method of the three fully mechanized coal mining machines as the research main line to solve the problems of state data preprocessing, cooperative control strategy learning and intelligent decision-making in the three fully mechanized coal mining machines, verifying the cooperative control decision-making model of the three fully mechanized coal mining machines with laboratory and field data. The specific contents are as follows:
(1) In view of lacking decision-making model of "three machines" collaborative control on intelligent fully mechanized coal mining face, through analyzing cooperative operation relationship and manual collaborative control process, determining the basic idea of collaborative control, and establishing the model and framework of collaborative control system for the three fully mechanized coal mining machines, the key technologies and solutions of the decision-making model are given, which lays the foundation for subsequent research.
(2) In view of noise pollution and redundancy of characteristic parameters in the status data in "three machines" in fully mechanized coal mining, a data cleaning method based on dynamic fusion of local abnormal factors and stacking denoising self-encoder (DFLOF-SDAE) is designed to improve the data quality. Based on the rough finite state machine model of fully mechanized "three machines" cooperative operation process, the original decision data table of multi-source data association for fully mechanized "three machines" cooperative control is established. The multi-label adaptive fuzzy neighborhood rough set and its characteristic parameter selection algorithm are proposed to select characteristic parameters for the decision data table of fully mechanized mining "three machines" with multi-label mixed data type. Finally, the accuracy and reliability of this method are verified by comparative experiments.
(3) In view of the problem of learning decision-making strategy of cooperative control of fully mechanized coal mining "three machines" with nonlinear, strong coupling and spatio-temporal correlation data, firstly, a new fuzzy rough width neural network model  based on width architecture is proposed, which can extract the hidden features of strongly coupled nonlinear data. Then, a decision strategy learning method for cooperative control of fully mechanized coal mining "three machines" based on fuzzy rough width neural network is constructed to obtain the time sequence feature information of input parameters and the spatial association feature information among multiple input parameters realizing the learning of cooperative control decision strategy of fully mechanized coal mining "three machines". The verification of laboratory data of fully mechanized coal mining "three machines" shows that compared with ELM, FRNN, BLS and LSTM methods, SL-FRBNN method has higher accuracy and reliability in the behavior decision of fully mechanized coal mining "three machines", the height adjustment of shearer drum, and the coordinated speed regulation of scraper conveyor and shearer.
(4) In view of the decision-making problem of "three machines" cooperative control in fully mechanized coal mining under dynamic unstable working conditions, a decision-making model of "three machines" cooperative control in fully mechanized coal mining based on incremental selective integration (ISEDFM-FRBNN&KELM) is proposed. The model is based on the idea of integrated decision-making. Firstly, Bootstrap resampling method is used to generate multiple training sets. Then, several binary mixed different base decision-maker models OIL-KELM&FRBNN with self-learning ability are constructed. Finally, the base decision-makers are integrated by the selective integration method of double error measures, to realize the online decision-making of "three machines" in fully mechanized mining. The method of ISEDFM is verified by laboratory data, and the results show that the proposed ISEDFM-FRBNN&KELM method can effectively recognize the behavior pattern of "three machines" in fully mechanized coal mining, predict the height of shearer drum and accurately predict the traction speed of shearer under the load change of scraper conveyor.
(5) To further verify the rationality and effectiveness of the decision-making model of "three-machine" cooperative control in fully mechanized coal mining, the algorithm is verified by the operation data of "three-machine" in 43101 fully mechanized coal mining face of Yujialiang Coal Mine. The results show that the decision-making method of "three-machine" cooperative control in fully mechanized coal mining is correct and feasible, and the cooperative and stable operation of "three-machine" is realized by applying the proposed characteristic parameter selection algorithm, cooperative control decision strategy learning algorithm and dynamic selective integrated decision-making method.
The decision-making method of multi-equipment cooperative control in intelligent fully mechanized coal mining face proposed in this dissertation provides a new solution for the autonomous cooperative control of "three machines" in fully mechanized coal mining face, makes a beneficial exploration for the intelligent development of fully mechanized coal mining face, and lays a foundation for realizing low-carbonization, intelligent and unmanned mining in coal mines.
In this dissertation, the decision-making method of multi-equipment cooperative control on comprehensive mining face is proposed, which provides a new solution for the autonomous cooperative operation of key equipment on comprehensive mining face and lays the foundation of low carbon, intellectualization and without worker on comprehensive mining face.
 

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