Research on Intelligent Quality Detection Method for Massive Industry Data Based on Mixture of Experts

Currently, massive data has become the core digital foundation for equipment manufacturing enterprises.Due to its characteristics such as multi-source heterogeneity, physical correlation, and spatiotemporal coupling, traditional data detection methods struggle to identify hidden logical conflicts and system-level anomalies.To address the quality detection challenges in complex data scenarios, driven by multi-source data, an intelligent detection method for massive industry data quality based on mixture of experts is proposed.An expert sub-library including regression analysis, clustering analysis, and semantic norms is constructed, and a self-adaptive routing mechanism based on gated networks is designed by combining the Top-k sparse activation routing strategy and the load balancing loss function.Experimental results show that this method has increased the F1 score of mixed data detection from 0.320 0 to 0.705 0, with a routing hit rate of Hit@1 reaching 0.973 7, and overcomes the performance limitations of a single detection model when processing mixed fields.