# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
## 4 Discussion  
**Case 1. ** The ship communication quality prediction task holds significant importance in enhancing the reliability and safety of maritime communication, thereby ensuring the security of maritime activities. Specifically, this task aids ship crews in better route planning and communication strategies, reducing the risks related to communication interruptions or failures and ultimately enhancing communication reliability. In emergency scenarios, ship communication quality prediction tasks can help anticipate the performance of emergency call signal transmissions under different environmental conditions. These predictions enable maritime rescue organizations to effectively determine the timing and direction of rescue operations, thereby improving overall operational efficiency. The knowledge graph constructed in this paper organizes multi-modal data describing the historical communication tasks of ships from a spatio-temporal perspective and uses communication event entities to represent the communication situations of ships during sea voyages. Specifically, communication events are divided into Data_Transfer_Event, Communication_Enhancement_Event, Disconnection_Event, Connection_Device_Event, Voice_Call_Event, and Message_Event, which include attributes such as occurrence time, communication channel, and communication result. The communication result describes the communication quality of the above events. Therefore, the ship communication quality prediction problem can be modeled as a task of completing missing attribute values of communication events in the multi-modal spatio-temporal knowledge graph. Suppose a fishing boat named \"Nanggang Fishery No. 1\" as shown in Figure 9 is fishing in the Huanghai Sea and suddenly encounters severe winds and waves, resulting in significant damage to the vessel. The crew urgently needs to send a distress signal to the nearby fishing boat \"Nanggang Fishery No. 2\". However, due to the complex maritime environment, the communication result attribute is missing in the communication event of this fishing boat, making it impossible to determine the quality of this communication. As shown in Figure 9, the blue area describes the communication scenario where \"At 8 June 2023 6:14, Nangang Fishery No. 1 encountered cloudy weather with strong winds of level 10-11 in the Huanghai Sea (37.25278\({}^{\circ}\) N, 120.46378\({}^{\circ}\) E) area and needed to use the NF1 ST0048 shortwave transmitter to send rescue information to the nearby Nangang Fishery No. 2 fishing vessel (37.43496\({}^{\circ}\) N, 121.0438\({}^{\circ}\) E)\". In this scenario, it is necessary to predict the communication quality result of this Message_Event to guide the rescue ship to  
Figure 9: Ship communication quality prediction based on the multi-modal spatio-temporal knowledge graph. adjust communication methods and rescue direction and complete the rescue operation in a timely manner. Through the analysis of historical data, it is evident that environmental factors such as weather conditions, wind speed, and geographic coordinates at a specific time can indeed impact the transmission quality of communication signals. Moreover, considering the extensive coverage of the Huanghai Sea and the high volume of maritime traffic, it is plausible that this fishing vessel may experience interference from other ships during communication. Thus, a deep learning approach based on graph convolutional neural networks can be employed to effectively address the missing communication result attribute for this specific communication event. This approach will enable the prediction of the communication quality, providing valuable insights into the effectiveness of the communication process. We trained a graph convolutional neural network model on historical data to address missing values in ship communication events.