基于知识图谱嵌入的应急救援决策推荐方法
Emergency Rescue Decision Recommendation Method Based on Knowledge Graph Embedding

在应急救援领域，快速准确地获取关键信息和制定有效的救援决策至关重要。传统的应急响应方法主要依赖于救援人员的个人经验和直觉判断，在紧急情况下难以迅速做出最优决策。本文提出了一种融合知识图谱、知识图谱嵌入TransR模型和协同过滤的应急救援决策推荐方法。该方法利用知识图谱技术整合灾害事件、应急任务、救援资源和历史案例等多源数据，使用TransR模型学习实体和关系的向量表示，最后结合基于用户的协同过滤算法思想，根据当前灾害态势信息与历史案例的向量余弦相似度为救援人员提供智能化的救援决策推荐服务。该方法考虑了应急信息的保密性和安全性，避免了对个人行为数据的依赖，从而降低了信息泄露和主观判断的风险。通过模拟实验验证了系统的有效性，结果表明该系统能够显著提高救援决策的准确性和响应速度。
In the field of emergency rescue, it is crucial to quickly and accurately obtain key information and make effective rescue decisions. Traditional emergency response methods mainly rely on the personal experience and intuitive judgment of rescue personnel, making it difficult to make optimal decisions quickly in emergency situations. This article proposes an emergency rescue decision recommendation method that integrates knowledge graph, knowledge graph embedding TransR model, and collaborative filtering. This method utilizes knowledge graph technology to integrate multi-source data such as disaster events, emergency tasks, rescue resources, and historical cases. It uses the TransR model to learn vector representations of entities and relationships, and finally combines the idea of user based collaborative filtering algorithm to provide intelligent rescue decision recommendation services for rescue personnel based on the vector cosine similarity between current disaster situation information and historical cases. This method considers the confidentiality and security of emergency information, avoiding reliance on personal behavioral data, thereby reducing the risk of information leakage and subjective judgment. The effectiveness of the system was verified through simulation experiments, and the results showed that the system can significantly improve the accuracy and response speed of rescue decisions.