钻探工程原始数据高效识别采集系统的开发与实现

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

摘要:

针对钻探工程原始数据采集效率低、人工录入错误率高、数字化转化不彻底等问题，本研究设计并开发了一套基于现代Web技术与多模态大模型的钻探原始数据高效识别采集系统。系统采用前后端分离的架构设计，整合Vue3.0、Spring Cloud Alibaba等主流技术栈，并创新性引入通义千问VL-max视觉语言大模型，构建了“移动端采集—云端智能识别—结构化存储”的全流程自动化处理体系，实现对纸质报表图像的精准识别与结构化解析。通过多场景实测验证，该系统在字段识别准确率、复杂表格适应性及系统鲁棒性方面表现优异，显著提升了钻探数据录入的效率与准确性，有效解决了传统采集方式的弊端。研究成果为钻探工程数据的数字化管理、共享复用及深度挖掘提供了关键技术支撑，对推动钻探行业数字化转型、构建智慧钻探大数据平台具有重要的工程价值与应用前景。

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.