Rapid developments in artificial intelligence technology have led to unmanned systems replacing human beings in many fields requiring high-precision predictions and decisions. In modern operational environments, all job plans are affected by emergency events such as equipment failures and resource shortages, making a quick resolution critical. The use of unmanned systems to assist decision-making can improve resolution efficiency, but their decision-making is not interpretable and may make the wrong decisions. Current unmanned systems require human supervision and control. Based on this, we propose a collaborative human--machine method for resolving unplanned events using two phases: task filtering and task scheduling. In the task filtering phase, we propose a human--machine collaborative decision-making algorithm for dynamic tasks. The GACRNN model is used to predict the state of the job nodes, locate the key nodes, and generate a machine-predicted resolution task list. A human decision-maker supervises the list in real time and modifies and confirms the machine-predicted list through the human--machine interface. In the task scheduling phase, we propose a scheduling algorithm that integrates human experience constraints. The steps to resolve an event are inserted into the normal job sequence to schedule the resolution. We propose several human--machine collaboration methods in each phase to generate steps to resolve an unplanned event while minimizing the impact on the original job plan.

翻译：人工智能技术的快速发展使得无人系统在诸多需要高精度预测与决策的领域逐渐替代人类。在现代作业环境中，所有工作任务均受到设备故障、资源短缺等突发事件的影响，亟需快速响应。利用无人系统辅助决策可提升处置效率，但其决策过程缺乏可解释性且可能产生错误判断，当前无人系统仍需人类监督与控制。基于此，我们提出一种人机协同处置非计划事件的方法，包含任务过滤与任务调度两个阶段。在任务过滤阶段，我们提出面向动态任务的人机协同决策算法，采用GACRNN模型预测作业节点状态、定位关键节点并生成机器预测的处置任务列表。人类决策者通过人机交互界面实时监控该列表，对机器预测结果进行修正与确认。在任务调度阶段，我们提出融合人类经验约束的调度算法，将事件处置步骤插入常规作业序列以完成调度。我们在各阶段设计了多种人机协同策略，在最小化对原作业计划影响的同时生成非计划事件的处置方案。