The occurrence of mine water disasters has brought huge losses. At present, the strategies and algorithms for mine water disaster rescue are mostly based on the Dijkstra algorithm, and usually do not consider the impact of individual differences among trapped individuals. In response to the impact of individual differences on rescue methods, this article analyzes the impact of water level and water flow velocity on human stability, as well as the prediction of the physical condition of trapped individuals. A method for calculating the risk coefficient is proposed, and a mine water disaster rescue model based on human characteristics is established. This article simulates a personalized rescue algorithm for Dijkstra underground optimal path rescue based on personal characteristics using Java programming language in the Eclipse development environment. The experimental results indicate that in mine water disaster rescue, selecting the shortest rescue path based on individual characteristics can fully utilize the capacity of the tunnel system and develop more accurate rescue plans for specific individuals.
Rail transit systems are an important part of public transportation in large cities. However, unforeseen emergencies such as floods, equipment failures, or large events can cause serious consequences such as traffic congestion and stranded passengers, thus affecting the normal operation of rail transit. To cope with these emergencies, this paper proposes a new algorithm that can query the latest reachable time under time constraints. A rail network model is developed to optimize Dijkstra's algorithm in emergency situations by using a new data structure. The study emphasizes the temporal complexity and spatio-temporal accessibility of the algorithm. Finally, the model and algorithm are validated using data from the Beijing Metro. The proposed shortest path planning emergency strategy for rail transit and the application of the algorithm are mainly aimed at the command center level of rail transit and solved practical problems.
Space division multiplexed (SDM) elastic optical network (EON) is considered to be a promising scheme for large-capacity optical communication networks. The static routing, modulation, spectrum, and space allocation (RMSSA) in SDM-EONs with bundles of single-mode fiber is studied. Considering the computational complexity of resource allocation formulation, a path-based integer linear programming (ILP) formulation with fewer variables and constraints is modeled to solve the static RMSSA problem. Then a heuristic algorithm named local optimal RMSSA (LO-RMSSA) is proposed to be applicable in large-scale network scenarios. The calculated metrics are the maximum index of utilized frequency slots, the local spectrum resource utilization, and the runtime of algorithms. The results show that the proposed ILP model and LO-RMSSA algorithm get higher computational efficiency with other metrics no worse than the existing one.
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