Automatic analysis of alarm embedded with large language model in police robot

Abstract

Police robots are used to assist police officers in performing tasks in complex environments, so as to improve the efficiency of law enforcement, ensure the safety of police officers and maintain social stability. With the rapid development of science and technology, police robots are widely used in the field of public security, such as alarm reception, patrol, explosive disposal, reconnaissance and so on. However, police robots still have the problem of analysis deviation in the process of receiving the alarm, which leads to the low efficiency of police dispatch. This study aims to enhance the police alarm automatic analysis ability of the police robots to assist in the dispatch of police. In this paper, we propose a novel method (FSTC-LLM) for sample augmentation based on large language model and noise reduction. The experimental evaluations are carried out on the alarm data set and the THUC News data set. The results show that the proposed FSTC-LLM has excellent performance in few shot text augmentation tasks, and can assist police robots to complete the task of automatic analysis of alarm with high quality, which is of great significance to enhance public security.

Keywords

• Police robot
• Large language models
• Few-shot learning