## 2 Related Work  
Although the impact of sun outage on ISLs is very significant, there is currently a lack of research on routing algorithms for sun outage interruptions in LEO constellations. For interruptions in satellite networks, most routing algorithms use backup paths. There are backup paths that respond in real time and backup paths that are calculated on demand. These algorithms can be divided into three categories. The first category uses different forms of shortest path algorithms to calculate alternative paths. The second category of algorithms calculates alternative paths for different exits in advance and uses active queue management (AQM) for path switching. The third type of algorithm finds the optimal backup path through different link models. These algorithms are prone to falling into local optimality and passing congestion to neighboring nodes and cannot effectively improve network performance. The LPRA algorithm proposed in this article takes advantage of the predictability of sun outage and the mesh structure of the LEO satellite network space segment to collect routing information in the failure area in advance and achieve load balancing through centralized routing calculations. The following sections compare the three types of algorithms with the LPRA algorithm. The ASER (area-based satellite routing) algorithm [9] adopts a distributed domain-based routing mechanism. Only a few intra-domain routing tables need to be updated for most topology changes, while the inter-domain routing table is updated only when inter-domain neighbor relationships change. This enables fast rerouting of dynamic networks. [PERSON] et al. [10] proposed an on-board autonomous control routing algorithm based on inter-satellite link status information. The satellite makes decisions for each hop based on the link status information with adjacent satellites and the network topology, optimizing the packet arrival rate and end-to-end time delay. OPSPF (Orbit Prediction Shortest Path First) [11] generates an instant routing table through periodic routing calculations, and uses an on-demand dynamic routing mechanism to cope with the impact of link failures [8]. These algorithms have less routing overhead. However, only using a static or dynamic shortest-path algorithm to calculate routes cannot effectively achieve load balancing. LPRA takes advantage of the predictability of sun outage, calculates routes during sun outage failure in advance and achieves load balancing in the failure area through an algorithm with a certain complexity. The ELB (Explicit Load Balancing) routing algorithm [12] adopts the method of dynamic interaction of link information. Neighboring satellites exchange queue usage status to indicate their current congestion status, and determine the forwarding path of the local and previous hop nodes based on the queue occupancy rate. Thereby reducing network congestion. The FSRS (fuzzy satellite routing strategy) algorithm [13] uses the Fuzzy Queue Congestion Index (FQCI) to reflect the degree of queue congestion, and uses the queuing theory and Naive Bayes methods to predict queuing delays, distinguish data packets with different QoS requirements and obtain effective paths that meet the needs. These algorithms are suitable to solve the problem of real-time link load jitter or link failure, avoid sudden traffic causing link overload and packet loss and have a fast response speed, but they can only achieve local load balancing. Routing loops and cascading congestion are easily formed in network environments with high loads [7]. In contrast, LPRA does not use a hop-by-hop approach for load balancing. Instead, it sets a limited area and redistributes traffic in links that are about to fail, achieving network load balancing on a larger scale. Thus, the network will not face routing loops and cascading congestion.