Application of Large Model Technology Based on Prompt Generation and Data Insights in Power System Data Analysis

This article studies the application of big model technology combining prompt generation and data insight in power system data analysis, aiming to improve the performance of Text to SQL to meet the complex needs of the power industry. Firstly, this article reviews the development of Text to SQL technology, including traditional rule-based methods and machine learning methods, and points out its shortcomings in handling complex queries. With the introduction of large models, especially the application of GPT series models, Text to SQL has made significant progress in natural language understanding and complex SQL generation. To address the challenges posed by complex queries and the characteristics of power data, this paper proposes a power data analysis framework that combines prompt generation and data insights. By utilizing prompt generation modules and data insight techniques, the model can automatically identify abnormal data and fault patterns, and provide predictive maintenance support. In addition, the Text to SQL method based on prompt generation dynamically generates similar prompts, combined with database schema information, effectively improving the ability to generate complex queries, especially suitable for scenarios involving multi table associations and complex query conditions.