~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。综采智能化作为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。综采工作面采煤机割煤与装煤，刮板输送机输煤与卸煤，二者协同作业将持续产生的煤流连续运出综采工作面，因此采运协同智能控制对实现综采智能化至关重要。但综采工作面环境恶劣，采煤机与刮板输送机耦合作用机理复杂，综采工作面采运协同智能控制已成为制约工作面智能化的主要因素之一。针对此问题，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”课题“大采高工作面智能开采安全技术集成与示范”（课题编号：2017YFC0804310），以综采工作面采运协同智能控制方法为研究主线，重点解决综采工作面煤流量在线监测问题、刮板输送机负载预测问题、综采工作面采运协同智能控制策略制定问题，通过建立现场数据采集系统对工作面采运协同智能控制方法进行验证，具体内容如下：
（1）综采工作面采运协同智能控制架构的提出。针对综采工作面采运协同智能控制方法缺乏问题，深入分析综采工作面煤流系统的生产过程与负载特点，确定综采工作面采运协同智能控制的基本思路，建立综采工作面采运协同智能控制模型与框架，给出综采工作面采运协同智能控制的关键问题与解决方案，为后续研究奠定基础。
（2）综采工作面煤流在线测量方法研究。针对暗光环境下综采工作面煤流量的动态实时监测问题，给出基于散斑结构光双目视觉的煤流量测量方案，并设计搭建煤流量测量装置；采用反向高斯牛顿算法对煤流散斑图像子区进行亚像素匹配，获得综采工作面煤流轮廓点云数据，结合四面体网格结构法对煤流量进行计算，最后通过煤流量测量实验对提出的系统性能进行验证。结果表明煤流量计算结果的相对误差最小为13.91%，能够准确、快速的测量刮板输送机的煤流量。
（3）综采工作面刮板输送机负载预测方法研究。针对刮板输送机电流数据有价值样本稀少和多样性不足导致的刮板输送机负载预测不准确的问题，通过分析综采工作面煤流系统的混合特性，提出扩展混合Petri网（ENPN），构建综采面煤流输送系统过程控制ENPN模型，生成突发工况下的刮板输送机电流数据样本；提出基于LSTM的条件Wasserstein对抗生成网络模型，对有价值的稀少刮板输送机电流数据样本进行扩充，建立完备充足的电流数据集；基于粗糙思想提出粗糙径向基神经网络RRBFNN模型，构建刮板输送机负载预测模型。
（4）综采工作面采运协同智能控制策略研究。针对综采工作面煤流系统的智能控制策略缺乏问题，提出基于煤流量在线测量和刮板输送机负载预测的综采工作面采运协同智能控制方法。该方法首先建立电流强化模型，得到反映综采工作面刮板输送机真实负载的电流分量；然后针对多输入参数中存在强非线性和时空关联特性，提出随机自注意力胶囊神经网络RSCAN，对综采工作面煤流系统运行状态进行特征提取；最后考虑煤流负载和煤流量建立基于随机自注意力胶囊神经网络RSCAN的综采工作面煤流系统智能控制模型，并进行仿真验证。本文采用的RSCAN方法建立的控制模型控制效果，比SCAN、CAN两种模型的平均绝对误差降低了17.7%，13.3%，结果表明，提出的负载电流预测方法和采运协同调速方法能有效实现综采工作面煤流系统的负载预测和采煤机的自适应调速。
（5）综采工作面采运协同智能控制模型现场数据验证。为进一步验证本文提出的综采工作面煤流系统智能控制模型的合理性和可行性，依托大柳塔煤矿52605综采工作面装备，构建综采工作面煤流量数据采集系统并进行数据采集和算法验证。结果表明：刮板输送机负载预测的平均误差为13.88%，3种采煤机运行位置工况中，机头附近的RSCAN采运协同控制效果最好，在数据集Ⅰ和数据集Ⅱ上的预测曲线与真实曲线之间的R-Squared值分别达到了0.8583和0.9046，精度提高了8.23%，表明本文提出的电流数据生成方法、煤流量测量方法、电流负载预测方法以及综采工作面采运协同智能控制方法合理可行，能有效实现综采工作面煤流输送系统的自主协同作业。
本文提出的综采工作面采运协同智能控制方法，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，为综采工作面的智能化和少人化发展奠定了基础。
 

~Coal mine intellectualization is the core technical support for the high-quality development of the coal industry and the inevitable direction of the technological revolution and upgrading development of the coal industry. As one of the key technologies of coal mine intellectualization, the realization of fully mechanized mining intellectualization will greatly promote the development of coal mine intellectualization. The shearer of the fully mechanized face cuts and loads coal, and the scraper conveyor transports and unloads coal. The two cooperate to continuously transport the coal flow out of the fully mechanized face. Therefore, in order to realize the intellectualization of the fully mechanized face, the intelligent control must be carried out for the mining and transportation cooperation process of the fully mechanized face. However, the environment of fully mechanized face is bad, and the coupling mechanism of shearer and scraper conveyor is complex. The collaborative intelligent control of mining and transportation in fully mechanized face has become one of the main factors restricting the intellectualization of the face. To solve this problem, this dissertation relies on the sub topic "Integrated and demonstration of intelligent mining safety technology in large mining height coalface" (grant No.:2017YFC0804310) of the national key research and development plan project "research and development of safety technology and equipment for intelligent mining of coal mine", and takes the collaborative intelligent control method of mining and transportation of fully mechanized mining face as the research main line, focusing on solving the problem of on-line monitoring of coal flow in fully mechanized mining face For the load prediction of scraper conveyor and the formulation of intelligent control strategy for mining and transportation coordination of fully mechanized mining face, the intelligent control method for mining and transportation coordination of working face is verified by establishing a field data acquisition system. The specific contents are as follows:
(1) The intelligent control model and framework of mining and transportation coordination in fully mechanized coal face are put forward. In view of the lack of intelligent control methods for mining and transportation coordination in fully mechanized face, this dissertation deeply analyzes the production process and load characteristics of coal flow system in fully mechanized face, determines the basic idea of mining and transportation coordination intelligent control in fully mechanized face, establishes the model and framework of mining and transportation coordination intelligent control in fully mechanized face, and gives the key problems and solutions of mining and transportation coordination intelligent control in fully mechanized face, Lay a foundation for follow-up research.
(2) Research on on-line measurement method of coal flow in fully mechanized coal face. Aiming at the problem of dynamic real-time monitoring of coal flow in fully mechanized face under dark light environment, a coal flow measurement scheme based on speckle structured light binocular vision is proposed, and a coal flow measurement device is designed and built; The Inverse Gauss Newton algorithm is used for sub-pixel matching of the coal flow speckle image sub-area to obtain the coal flow contour point cloud data of the fully mechanized mining face. Combined with the tetrahedral grid structure method, the coal flow is calculated. Finally, the performance of the proposed system is verified by the coal flow measurement experiment. The results show that the proposed method and system can accurately and quickly measure the coal flow of the scraper conveyor.
(3) Research on load forecasting method of scraper conveyor in fully mechanized mining face. In view of the inaccurate load prediction of the scraper conveyor caused by the scarcity of valuable samples and the lack of diversity of the scraper conveyor current data, by analyzing the mixing characteristics of the coal flow system in the fully mechanized face, an extended hybrid Petri net (ENPN) is proposed to build the process control ENPN model of the coal flow system in the fully mechanized face, and generate the scraper conveyor current data samples under sudden working conditions; A conditional Wasserstein antagonism generation network model based on LSTM is proposed to expand the valuable rare current data samples of scraper conveyor and establish a complete and sufficient current data set; Based on the rough idea, a rough radial basis function neural network RRBFNN model is proposed to build a scraper conveyor load prediction model.
(4) Research on intelligent control strategy of mining and transportation coordination in fully mechanized coal face. In view of the lack of intelligent control strategy of coal flow system in fully mechanized coal face, a collaborative intelligent control method based on on-line coal flow measurement and scraper conveyor load prediction is proposed. In this method, firstly, the current intensification model is established to obtain the current component that reflects the real load of the scraper conveyor in the fully mechanized mining face; Then, in view of the strong nonlinearity and spatiotemporal correlation in the multi input parameters, a random attention capsule neural network RSCAN is proposed to extract the characteristics of the coal flow system in the fully mechanized face; Finally, considering the coal flow load and coal flow, the intelligent control model of coal flow system in fully mechanized coal face based on stochastic attention capsule neural network RSCAN is established and verified by simulation. The results show that the proposed load current prediction method and the mining and transportation coordinated speed regulation method can effectively realize the load prediction of the coal flow system in the fully mechanized face and the adaptive speed regulation of the shearer.
(5) Field data validation of the intelligent control model for mining and transportation coordination in fully mechanized coal face. In order to further verify the rationality and feasibility of the intelligent control model of the coal flow system in the fully mechanized face proposed in this dissertation, relying on the equipment of the 52605 fully mechanized face in Daliuta coal mine, the coal flow data acquisition system in the fully mechanized face is constructed and the data acquisition and algorithm verification are carried out. The results show that the average error of scraper conveyor load prediction is 13.88%, and the maximum R-squared value of mining and transportation collaborative control is 0.9046;It shows that the current data generation method, the coal flow measurement method, the current load prediction method and the mining and transportation collaborative intelligent control method proposed in this dissertation are reasonable and feasible, and can effectively realize the autonomous collaborative operation of the coal flow and transportation system in the fully mechanized mining face.
The intelligent control method proposed in this dissertation provides a new solution for the autonomous cooperative operation of key equipment in fully mechanized face, and lays a foundation for the development of intelligence and less people in fully mechanized face.
 

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