基于知识图谱的电磁干扰源定位方法

为解决现有电磁大数据管理手段单一，无法充分利用电磁数据的弊端，引入分区管理的概念，采用聚类算法将电磁数据按地理属性进行分区，利用图数据库对电磁数据进行管理，将电磁聚类后的结果转化为知识图谱实体，抽取实体之间的关系，发掘电磁数据间的潜在关联。针对电磁干扰源定位难、效率低下的问题，提出了以知识图谱为基础，大数据实时处理技术为辅助的改进接收信号强度指示(RSSI)定位算法，并实验仿真了真实电磁数据下干扰源定位的过程，分析了单目标干扰源和多目标干扰源下定位的性能。实验结果表明，所提的基于知识图谱的电磁干扰源定位方法较传统RSSI定位方法效果更显著，误差更小。

Location of electromagnetic interference source based on knowledge graph

The existing electromagnetic big data management method is single and unable to make full use of electromagnetic data. The concept of partition management is introduced. The electromagnetic data is partitioned by geographic attributes using a clustering algorithm and managed by the graph database. The knowledge graph entities are transformed from the electromagnetic clustering results, and then the entity relationships are extracted to explore potential relationships among electromagnetic data. Aiming at the problem of the difficulty and low efficiency of electromagnetic interference source positioning, an improved Received Signal Strength Indication(RSSI) positioning algorithm based on knowledge graphs and big data real-time processing technology is proposed. The experiment simulates the process of interference source location under real electromagnetic data, and analyzes the performance of single-target interference source and multi-target interference source positioning. The experimental results show that the proposed method for locating electromagnetic interference sources based on the knowledge graph is more effective than the traditional RSSI locating method, and its error is smaller.