A Large-Small Models Engaged Knowledge Reasoning Method for High Consequence Area Intelligent Detection of Oilfield Pipelines

Addressing the challenges of insufficient utilization of multi-source data and poor adaptability to dynamic environments in intelligent detection of oilfield pipeline high consequence areas, a cross-modal collaborative detection framework based on knowledge reasoning is proposed. Firstly, a visual small model is developed to quickly locate sensitive targets around pipelines in remote sensing images. Meanwhile, a multimodal large model is deployed to conduct in-depth semantic association analysis of geospatial data and historical operation records. Moreover, a bidirectional interaction mechanism between the perception data of the small model and the prior knowledge base of the large model is established through a knowledge transfer enhancement module to achieve online adaptive calibration of cross-modal features. The experimental results show that the proposed architecture effectively breaks through the perception-cognition segmentation bottleneck of single-model detection and single-modal data. In the actual high consequence area detection scenario of oilfield pipelines, the accuracy is improved by 12.7% compared with that of a single model, and the detection speed is more than 20 times faster than that of manual recognition, providing reliable intelligent decision-making support for the safety management of oilfield pipelines.