  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.
