## 1 Introduction  
With global climate change, the frequency and intensity of extreme weather events has increased significantly. Ice disasters are a typical extreme weather phenomenon that represent one of the major hazards affecting the normal operation of cities and communities, Not only can paralysis of the power and water supply systems result in drastic deterioration of living conditions, it may also poses life-threatening risks under extreme circumstances [1, 2]. Furthermore, ice disasters can cause long-term damage to infrastructure, worsen energy supply shortages, and lead to uneven water resource distribution [3, 4]. Thus, how to efficiently dispatch emergency resources under ice disasters while ensuring the rapid restoration of normal supply post-disaster has become a critical issue in the emergency resource management. Currently, traditional emergency resource dispatch strategies primarily rely on post-disaster response and static resource allocation information. Mobile energy storage vehicles and other necessary devices are allocated into different locations after the disaster occurs. However, because ice disasters often strike with sudden onset and prolonged impacts, traditional static resource scheduling methods exhibit significant limitations [5; 6]. Studies have shown that the unpredictability and persistence of ice disasters render conventional static dispatch schemes inefficient in the recovery process, often failing to respond promptly to the urgent needs of affected areas [7]. Therefore, emergency management strategies based on pre-disaster prediction and dynamic scheduling have gained increasing attention. By leveraging pre-disaster information forecasting and resource preparedness, these strategies enable proactive resource allocation, thereby reducing the complexity of post-disaster scheduling and improving resource utilization efficiency [8]. Existing research primarily focuses on post-disaster resource allocation and recovery strategies, while the integration of pre-disaster predictive information to optimize resource dispatch under ice disaster conditions remains an area that has yet to be fully explored [9; 10; 11]. Existing studies have explored the introduction of dynamic scheduling methods [12; 13] and path selection [14; 15]. However, the application of these methods in ice disasters remains relatively limited, lacking both field verification and targeted strategies. Research has shown that mobile power sources, repair personnel, and network configurations can be coordinated to accelerate load restoration in power distribution systems [16; 17; 18; 19]. Nevertheless, studies focusing specifically on mobile power dispatch are relatively scarce, and they rarely explore the feasibility and advantages of other mobile resources in enhancing the resilience of energy systems. To address these issues, in this paper we propose an emergency resource dispatch strategy for ice disasters based on pre-disaster prediction and dynamic scheduling to improve resource allocation efficiency. Post-disaster scheduling costs are reduced by rationally grouping communities prior to the disaster and optimizing resource deployment and dispatch routes based on ice disaster trajectory predictions. Specifically, the fast Newman algorithm [20] is employed to cluster communities, thereby reducing the complexity of resource scheduling and optimizing resource allocation pathways. Furthermore, by integrating ice disaster trajectory predictions, we introduce both mobile energy storage vehicles and mobile water storage vehicles to enhance the efficiency and accuracy of post-disaster resource supply while ensuring stable energy and resource availability [21]. The implementation of this strategy not only significantly reduces the cost of community energy self-sustainability but also improves user satisfaction [22] while maintaining system stability under various disaster scenarios. The main contributions of this paper are summarized as follows.