Navigation warfare has become one of the new electronic combat styles, and it effectively supports joint operations. However, the satellite navigation landing signal is weak and highly vulnerable to interference, which affects various command chains of joint operations and has a devastating impact on the war. This paper proposes an alternative defense style for unmanned aerial vehicle (UAV) pseudo satellite network navigation when satellite navigation is rejected. Then a multi-point navigation power intelligent enhanced defense style is presented when navigation signals are subject to electromagnetic interference. Next, an autonomous integrated navigation defense style is put forward when satellite navigation signals are unstable. Finally, this paper proposes in-orbit autonomous programming and navigation signal reconstruction defense styles when the satellite navigation signal frequency band is attacked. Thus, effective defense can be carried out from multiple perspectives, ensuring the normal broadcasting of navigation signals, and effectively supporting the smooth progress of various combat activities.

Intelligence is a systematic and ecological concept, involving knowledge and understanding in multiple disciplines and fields, with differences and characteristics in different cultural and historical backgrounds. Intelligence is systematic yet diverse. Different agents have different types, characteristics, and expressions of intelligence. Intelligence has both advanced and ordinary parts. Advanced intelligence includes complex technologies such as deep learning and natural language processing, while ordinary intelligence includes basic pattern recognition, data processing and other tasks. This multi-level intelligence covers different fields and application needs, and has a positive impact on human life and work. Intelligence is divided into a technological part and an artistic part. Intelligence is both materialistic and idealistic, and is a complex system involving the interaction of physical, biological and social factors. This article also explores the ontology, methodology and epistemology of intelligence, focusing on the nature and existence of intelligence, how to build and implement intelligent systems, and the interaction and cognitive process between intelligent agents and the external world. Researches at these three levels are interrelated and jointly promote the development of the field of intelligence. As a representation of the interaction between human-machine environment system, intelligent systems are composed of humans, machines and the environment. The interaction between them is the basis for the operation and development of intelligent systems. Intelligence has different influences on eastern and western cultures. Intelligence is the product of human evolution, shaping human thinking, behavior and civilization, making humans the most creative and adaptable species on earth. Intelligence contains both mathematical and nonmathematical parts, both changing and unchanging parts. The existence of these multiple components enables the intelligent agent to better adapt to and respond to different situations and achieve higher performance and efficiency.

In recent years, the rapid development of artificial intelligence has had an important impact on mental health services such as psychological counseling and psychological assessment. In this paper the accuracy and methods of artificial intelligence in psychological assessment as well as ethical issues are reviewed. Based on the development of multi-modal and psychological intervention technology, the future application research of artificial intelligence in psychological assessment is forecast. It is believed that the application of artificial intelligence in psychological assessment in the future will pay more attention to individual needs and interdisciplinary cooperation. It will be more widely used in information collection, data analysis, humancomputer interaction technology, and non-invasive regulatory technology cooperation, and the ethical research of artificial intelligence will play a more important role in the future.

The various risks caused by the development of artificial intelligence (AI) technology are increasingly pervasive in every stage of innovation activities. It is a vital factor that we should take prospective and proactive reflection on the potential risks associated with the development of artificial intelligence technologies as we strive for advancements in this field. Based on the global development of artificial intelligence technology, technological and industrial advancement and the theoretical perspective of AI technology catch-up, this article analyzes the relationship between technological catch-up in artificial intelligence and potential risks from three dimensions: cognition risk, Scenario risk, and competition risk. This study concludes that risk perception should be embedded into the theoretical framework of AI technology catch-up, and more attention should be paid on the diversity of technology catch-up modes under the risk segmentation spectrum of different AI application scenarios. In addition, game risk in AI has been another key factor in restricting the efficiency of AI technology catch-up as the increasing negative externality caused by the dynamic and complex interaction of "technology-political-risk".

The application of artificial intelligence, represented by face recognition payment, has improved efficiency and optimized user experience, but it has also introduced various risks. In order to regulate its development, it is essential to investigate the factors that influence the adoption of face recognition payment. Current research on the impact of perceived risk facets on face recognition payment remains limited. This study, based on the UTAUT model, analyses the key factors influencing the intention to use face recognition payment (performance expectancy, effort expectancy, social influence, and facilitating conditions) and further examines the influence of five perceived risk facets (time risk, privacy risk, legal risk, financial risk, and health risk) on performance expectancy and effort expectancy. A structural equation analysis of 412 valid survey responses shows that the four factors in the UTAUT model have a significant positive impact on the behavioral intention to use face recognition payment. Privacy risk and financial risk are the facets of users' greatest concern, and both have a significant negative impact on performance expectancy and effort expectancy. This study identifies the specific mechanisms through which different risks affect the adoption of face recognition payment, providing reference and empirical evidence for its risk management and governance.