A Rescue Task-Chain Construction Method for UAV-Assisted Heterogeneous Emergency Rescue Systems

In sudden disaster scenarios, the rapid strategy-driven task-chain construction of rescue task chains in UAV-assisted heterogeneous emergency rescue systems is critical for an effective emergency response. Such systems usually involve multiple types of rescue entities, such as reconnaissance UAVs, delivery UAVs, decision centers, and other emergency resources, which provide four-dimensional capabilities including reconnaissance, intelligence analysis, command and control, and rescue disposal. However, existing manual planning and heuristic optimization methods often suffer from low online decision efficiency in large-scale disaster scenarios and rarely examine how different task-chain construction strategies affect task-chain construction. To address this problem, this paper studies a strategy-aware rescue task-chain construction problem for UAV-assisted heterogeneous emergency rescue systems, in which heterogeneous rescue entities are mapped to reconnaissance, intelligence-analysis, command, and disposal nodes according to their four-dimensional capabilities under capability, distance, cost, availability, and strategy constraints. The problem is formulated as a constrained combinatorial optimization problem, and eight task-chain construction strategies are designed from three dimensions: command mode, information-sharing scope, and disposal mode. To solve this problem, a deep reinforcement learning framework integrating a pointer network and entropy-regularized advantage actor–critic (A2C) is proposed. The pointer network generates variable-length entity-selection sequences for task chains, while entropy-regularized advantage actor–critic (A2C) optimizes the selection policy using an entropy-regularized objective that considers success probability, execution cost, and resource utilization. Experiments across 72 scenarios show that the proposed method outperforms the genetic algorithm in terms of timeliness and overall multi-indicator performance, especially in large-scale rescue scenarios. The results further indicate that distributed command strategies achieve a more balanced and robust performance, providing practical guidance for strategy selection and resource scheduling in UAV-assisted heterogeneous emergency rescue. Specifically, compared with the genetic algorithm under the full-scale 72-scenario evaluation, the proposed method improves the cost-control indicator E2 by 51.2% and the topological indicators E4, E5, E6, and E7 by 86.8%, 346.5%, 375.7%, and 421.5%, respectively, all while reducing the online decision time from 36.5 s to 3.0 s (a 91.8% speedup). The genetic algorithm still outperforms the proposed method on the success-rate indicator E1 and entity-utilization indicator E3, which is discussed as a limitation of the proposed method in the discussion of applicability.