摘要
堤防工程作为防洪体系的重要组成部分，其安全性直接关系到人民生命财产安全。本文围绕“基于机器学习的堤防工程隐患智能检测技术”展开研究，提出将现代机器学习方法引入传统水利工程检测领域，以提升隐患识别的准确性与效率。通过构建适用于堤防结构特征的数据模型，并结合深度学习与图像识别技术，实现对裂缝、渗漏等典型隐患的自动识别与分类。该技术方案在实验环境中展现出较高的识别准确率，具备良好的应用前景。
关键词： 机器学习；堤防工程；隐患检测；智能识别；数据模型
Abstract
As an important component of the flood control system, the safety of dike engineering is directly related to the safety of people's lives and property. This paper focuses on the research of "intelligent detection technology for hidden dangers in dike engineering based on machine learning", and proposes to introduce modern machine learning methods into the field of traditional water conservancy engineering detection to improve the accuracy and efficiency of hidden danger identification. By constructing a data model suitable for the structural characteristics of dikes, and combining deep learning with image recognition technology, the automatic identification and classification of typical hidden dangers such as cracks and seepage are realized. The technical scheme shows high recognition accuracy in the experimental environment and has good application prospects.
Key words： Machine learning; Dike engineering; Hidden danger detection; Intelligent recognition; Data model
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