Development and implementation of an efficient recognition and acquisition system for drilling engineering raw data

Abstract:

To address the problems of low efficiency， high error rate in manual entry， and incomplete digital transformation in the collection of raw data from drilling engineering， this study designs and develops an efficient recognition and collection system for drilling raw data based on modern Web technologies and multimodal large models. The system adopts a front-end and back-end separation architecture， integrates mainstream technology stacks such as Vue 3.0 and Spring Cloud Alibaba， and innovatively introduces the Qwen-VL-max vision-language multimodal large model. It constructs a full-process automated processing system of "mobile terminal collection-cloud-based intelligent recognition-structured storage"， realizing accurate recognition and structured parsing of paper report images. Verified through multi-scenario practical tests， the system performs excellently in terms of field recognition accuracy， adaptability to complex tables， and system robustness， significantly improving the efficiency and accuracy of drilling data entry and effectively solving the drawbacks of traditional collection methods. The research results provide key technical support for the digital management， sharing and reuse， and in-depth mining of drilling engineering data， and have important engineering value and application prospects for promoting the digital transformation of the drilling industry and building an intelligent drilling big data platform.