Commonly used UAV emergency inspection methods are executed by the instructions of the ground command center. The response rate depends on the stability of the communication network and the rapid response ability of the commander. The critical time window is fleeting, which is likely to cause unnecessary loss. Crisis rapid response capability has become the key to measuring system capabilities. In order to improve the system’s rapid response capability, a method of deploying decision-making agents on airborne computers and ground early warning systems is proposed. This early warning method uses key technologies such as multi-network integration, situation assessment, neural network architecture, deep learning, reinforcement learning, and intelligent cognitive reasoning to effectively ensure the effectiveness of crisis warning. The early warning method of the early warning system is as follows: the mission computer uniformly collects the flight control status parameters, the load status parameters and the load real-time data form a composite information flow. The task computer adopts the methods of protocol conversion, data classification, and danger recognition to the compound information flow to identify the crisis information and make a preliminary analysis and judgment of the crisis state. If it is determined that it is necessary to track the target in real time, the initial task assignment and parameter adjustment of the load are carried out, and the continuous tracking of the task target is carried out to realize the rapid response to the crisis on the edge side. At the same time, the composite data are downloaded to the command center. The command center performs the secondary crisis analysis and risk level determination and outputs the crisis plan deduced by the agent to realize the strategy assistance. The accuser refers to the plan strategy and issues instructions to the task computer, and the task computer receives it. Instruction and secondary adjustment and optimization of the load parameters. If there is a flight route adjustment instruction, the adjustment route will be sent to the flight controller, which greatly improves the flexibility and efficiency of handling the crisis in the UAV inspection process. By adopting this set of early warning methods, it can provide users with an updated, faster and more efficient way to realize the early warning requirements in drone inspections, which is a new breakthrough in the field of drone command methods.
Real-Time Warning and Risk Assessment of Tailings Dam Disaster Status Based on Dynamic Hierarchy-Grey Relation Analysis
