Method for extracting power emergency plan information based on LLM prompt learning

The Large Language Model (LLM) as a representative of generative artificial intelligence, demonstrates strong capabilities in natural language comprehension, which was recently put into engineering applications in the field of power emergency. The author proposes a method of extracting information from power emergency plans by leveraging its emergent abilities and prompt learning techniques. By this method, custom-defined contents can be extracted from power emergency plans and linked to the corresponding personnel to generate executable task instructions. The results indicated that this method can accurately extract the custom-defined information from power emergency plans and applys to different LLMs. And the stronger the emergent abilities of the LLM, the more accurate the information extraction is. The method is proofed to effectively assist power emergency personnel in making decisions and expected to be used in various practical scenarios.