A Smart Data Processing Method for Distribution Terminal Based on Structural Perception and Logical Constraints

This paper addresses the issues of low efficiency and inadequate accuracy in logical verification for intelligent distribution network terminal debugging data processing. We propose an intelligent processing method that integrates structural perception and logical constraints. A multimodal graph attention network (GAT) is constructed to achieve adaptive parsing of non-standard point tables, while a hybrid reasoning mechanism combining symbolic rules and knowledge graph embedding (KGE) is employed for data consistency verification. Experimental results on real business data from Guizhou Power Grid show that our method achieves 93.7% accuracy in point table parsing, 94.8% logical error detection rate, and reduces single-terminal data processing time from 46 minutes to 8.7 minutes. The research provides effective technical support for the digital transformation of distribution terminal debugging.