Application of Baidu Comate-based AI technology to the automatic numbering of sampling points in irregular geochemical networks
WANG Xuan1,2(), YANG Huan1,2, WANG Ran1,2, LI Ying1,2(), WANG Hai-Peng1,2, LIU Yan-Song1,2, LIAO Jun-Yu1,2, ZHANG Cheng-Bin1,2, ZHANG Xu-Dong1,2
1. Liaoning Institute of Geological Exploration Co., Ltd., Dalian 116100, China 2. Liaoning High-Resolution Observation System Application and Service Center of Geological Resources and Environmental, Dalian 116100, China
In the era of rapid digitalization development, artificial intelligence (AI) technology has brought revolutionary changes to traditional work patterns. Based on Baidu Comate, this study proposed an automatic numbering method for sampling points in irregular geochemical networks. Automatic numbering tests, conducted on 12 000 geochemical sampling points, demonstrate that the method improved the efficiency by 99.8% and achieved 100% accuracy compared to the traditional manual method. This indicates that the proposed method is more efficient and accurate than traditional approaches, effectively avoiding human errors and improving work efficiency. This study also discussed the challenges AI faces in processing complex instructions, the importance of instruction clarity, the identification of complex logic, and the necessity of developing knowledge reserves. Although AI technology has significantly improved the efficiency and accuracy of the automatic numbering of sampling points in irregular geochemical networks, the early development of packaging tools requires personnel who can read codes to modify and verify the codes. Additionally, AI-assisted demand processing should be in phases, and ultimately, it is necessary to encapsulate verified codes into a tool for reuse.
王烜, 杨欢, 王然, 李英, 王海鹏, 柳岩松, 廖俊宇, 张呈彬, 张旭东. 基于Baidu Comate的AI技术在化探不规则网采样点自动编号中的应用[J]. 物探与化探, 2025, 49(2): 462-469.
WANG Xuan, YANG Huan, WANG Ran, LI Ying, WANG Hai-Peng, LIU Yan-Song, LIAO Jun-Yu, ZHANG Cheng-Bin, ZHANG Xu-Dong. Application of Baidu Comate-based AI technology to the automatic numbering of sampling points in irregular geochemical networks. Geophysical and Geochemical Exploration, 2025, 49(2): 462-469.
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