Intelligent emergency information generation technology based on incremental fine-tuning of large-scale power grid control models

In emergency dispatching of new power systems, traditional emergency information reporting relies on manual statistics and transmission, which generally suffers from low efficiency, easy information omissions, and poor scenario adaptability. This paper proposes an intelligent emergency information generation technology based on incremental fine-tuning of a large-scale power grid control model. First, a multi-scenario emergency corpus is constructed, and an emergency-specific knowledge base is established in conjunction with power industry standards. Second, an incremental fine-tuning strategy oriented towards emergency management tasks is designed. Through domain data alignment, hierarchical parameter optimization, and regularization constraints, the adaptability to the semantics of power grid emergency scenarios is enhanced while maintaining the general large-scale model language understanding capability. Finally, based on the refined model, structured emergency information generation is realized. Experimental results show that this technology can significantly improve the accuracy and timeliness of emergency information generation, effectively reduce the cost of manual intervention, and provide efficient and reliable information support for power grid emergency dispatching. This is of great significance for promoting the transformation of power emergency management towards intelligence and automation.