Recent world wars and local conflicts have shown that drone swarm operations bring unprecedented threats to position-based air defense， which is profoundly changing modern information warfare and will become an important component in the future battlefield. In view of the unprecedented threats brought by drone swarm operations to the core position air defense， this paper analyzed the typical styles and weaknesses of drone swarm operations. On this basis， with the cyber-electronic countermeasure forces as the starting point， the typical scenarios for anti-drone swarm operations and the composition of the cyber-electronic countermeasure forces were presented. According to the “detectable， disruptive， and defendable” overall strategy， the operational processes and typical application methods of cyber-electronic countermeasure forces in anti-drone swarm operations were analyzed. The research results can provide theoretical guidance and technical support for position air defense against anti-drone swarm operations.

The analysis of radar fault cause text is helpful to locate the fault location， facilitate equipment maintenance and analyze equipment performance. Knowledge extraction， representation and management of radar fault cause text using knowledge graph technology can effectively improve the utilization efficiency of the text， quickly locate the fault location and analyze the equipment performance in time. On the basis of summarizing the characteristics of radar equipment fault cause text， radar equipment fault cause knowledge graph is constructed by a method combines both the scheme layer and the data layer. The scheme layer of the knowledge graph in the top-down style is designed， which defines the knowledge framework， the concept types， and the relationships between the concepts of the knowledge graph. For the text characteristics， the data layer of knowledge map is constructed in the bottom-up style： in order to solve the problems of small sample size and compound words entity recognition， entity naming recognition is carried out by Att-AlBERT-BiGRU-CRF model. The ALBERT-BiGRU-Att model is used for relationship extraction. Experiments show the effectiveness of the above extraction method. Based on the entities and relationship， the knowledge graph of radar fault causes is constructed.