To address the real-time and efficiency requirements for multi-target cooperative interception by unmanned combat systems in dynamic battlefield scenarios， this paper proposes a greedy target assignment and fire scheduling method based on dynamic time window （GTAFS-DTW）. By constructing a relative motion model between combat units and targets， the proposed method incorporates engagement distance constraints， task execution constraints， and target maneuver characteristics. a dynamic time window screening mechanism is designed with the optimization objective of minimizing engagement time and impact time difference. On this basis， a hierarchical greedy strategy is adopted to prioritize unit-target pairs with overlapping feasible engagement intervals， and temporal cooperative optimization is achieved through iterative adjustment of window boundaries and fire scheduling. Simulation results demonstrate that GTAFS-DTW effectively reduces impact time deviation to within 0.5 s in both lateral and longitudinal queue scenarios compared with traditional discrete particle swarm optimization （DPSO）， significantly enhancing computational efficiency and satisfying real-time optimization requirements.

In modern combat， nodes differ significantly from the single-function nodes of the past， evolving towards multi-function integration. To address issues such as the characterization of node function diversity， the dynamic reconfiguration of system models， and realistic simulation of enemy network analysis in current node importance evaluations of combat systems， a dynamic reconfiguration model based on unit attributes for the enemy’s network is proposed. The concept of tactical weight is introduced， and multi-function integration of nodes and edges is achieved through attribute induction and attribute assignment. A method and algorithm for calculating the dynamic reconfiguration of network effectiveness are presented. Taking into account the actual operational context and efficiency， the node attack cost is incorporated， and a node importance evaluation model based on the contribution rate to system network effectiveness is proposed. In simulation experiments， node importance is evaluated using seven different metrics， including node effectiveness index， degree centrality， and betweenness centrality， under various spatiotemporal conditions. The decline in network effectiveness after network strikes is also analyzed. The simulation results demonstrate that the proposed method enables dynamic reconfiguration of the network post-attack， reflecting the network’s strong robustness and confirming the validity of the proposed algorithm.