我国煤矿智能化综采工作面建设处于初级阶段，液压支架电液控自动化技术已广泛应用，但目前液压支架单一固化的自动化控制逻辑难以适应复杂、多变、动态的工作面生产场景，现场实际控制过程仍大多采用自动化+人工干预方式。针对综采工作面复杂场景中液压支架人机交互协作任务需求，提出液压支架中部跟机多模态人机协同控制系统的理论方法与技术原理。首先，设计了人工式、分工式、批准式、否决式4种人机协同模态，以煤层地质、瓦斯粉尘、采煤机速度、液压支架智能水平、系统状态、岗位工技术水平、任务负荷等为判别因素，构建了基于AOG的液压支架人机协同模态选择模型，实现了人工、机器偏好的模态选择。然后，设计了液压支架中部跟机人机协同控制决策机制，提出液压支架中部跟机再次调控策略AI推理技术思路，利用现场数据学习人工操作经验，分别构建了液压支架是否再次调控决策树分类模型和再次拉架时间贝叶斯回归模型，基于上述模型开发了液压支架人机协同控制决策程序，实现了基于多模态与或推理和再次调控策略AI推理的液压支架人机协同控制。最后，采用云−边−端架构软硬件技术，开发了液压支架多模态人机协同控制系统，实现了模型进化、运算推理、程序执行等液压支架多模态人机协同控制功能。该系统在沙曲二矿3404工作面进行了工业试运行，相比于系统使用前，该工作面液压支架中部跟机效率平均提高2%。

The construction of intelligent fully mechanized mining faces in China’s coal mines is in its early stages, and the automation technology of hydraulic support electro-hydraulic control has been widely applied. However, at present, the automation control logic of a single solidification of hydraulic supports is difficult to adapt to complex, ever-changing, and dynamic production scenarios. The actual control process in mine face still mostly adopts a collaborative approach of automation and manual intervention. In response to the demand for human-machine interaction and cooperation tasks in the complex scenarios of fully mechanized mining faces, a theoretical method and technical principle of a multimodal human-machine collaborative control system for hydraulic supports following the shearer in the middle range mining face (hereinafter referred to as hydraulic supports following) was proposed. Firstly, four human-machine collaborative modes, namely manual, division of labor, approval, and veto, were designed. Based on the factors such as coal seam geology, gas and dust, shearer speed, hydraulic support intelligence level, system status, job technical level, and task load, an AND-OR graph model for hydraulic support human-machine collaborative mode selection was constructed, which achieved a modal selection with manual or machine preferences. Then, a human-machine collaborative control decision-making mechanism for hydraulic supports following was designed. On this basis, an AI inference technology for the secondary control strategy of hydraulic supports following was proposed. Specifically, using on-site data to learn manual operation experience, a decision tree classification model for whether the hydraulic support should be secondary controlled and a Bayesian regression model for estimating the secondary control time of pulling hydraulic support were constructed. Based on the above model, a human-machine collaborative control decision-making program for the hydraulic support following was developed, which achieved human-machine collaborative control of hydraulic supports following based on AND/OR inference for modal selection and AI inference for secondary control strategy. Finally, using cloud edge end architecture software and hardware technology, a multimodal human-machine collaborative control system for hydraulic supports following was developed, which achieved the control functions such as model evolution, operational reasoning, and program execution. The system has undergone industrial trial operation on the 3404 mining face of Shaqu No.2 Mine. The result shows the efficiency of the hydraulic support following has increased by an average of 2% compared to that before. This paper forms an efficient and safe human-machine interactive decision-making mechanism for the comprehensive mining equipment group, which will provide practical theoretical methods and feasible technical paths for the development of intelligent comprehensive mining working faces.