The operation risks of water diversion projects involve numerous influencing factors, complex interrelationships, and heterogeneous data from multiple sources. This study presents a multimodal knowledge graph construction approach for water diversion projects, aiming to comprehend and identify the key risks associated with engineering operation and their propagation patterns. Utilizing term-masked pre-trained language models enhances comprehension of specialized terminology and identifies risk entities within the text. Employing high-order residual convolutional neural networks improves the processing capability for complex graph data, extracting risk information from images. Aggregating multimodal knowledge graphs based on the semantic relationships among entities and conditional probability to determine the coupled features of different risks. Employing complex network theory, analyze node degree and betweenness centrality to identify the diffusion effects and propagation levels of risks. The results indicate that the knowledge extraction accuracy of our method is high (with an average F1 score of 95.85%), enabling the qualitative analysis and quantitative calculation of operational risks in engineering. Relevant studies can effectively enhance the reliability of engineering safety management and reduce the impact of engineering risks on water supply security.

中文翻译：

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引水工程风险识别的多模态知识图谱构建

引水工程运行风险影响因素众多、相互关系复杂、数据来源多样。本研究提出了引水工程的多模态知识图谱构建方法，旨在理解和识别与工程运营相关的关键风险及其传播模式。利用术语屏蔽的预训练语言模型可以增强对专业术语的理解并识别文本中的风险实体。采用高阶残差卷积神经网络提高了复杂图数据的处理能力，从图像中提取风险信息。基于实体之间的语义关系和条件概率聚合多模态知识图谱，以确定不同风险的耦合特征。运用复杂网络理论，分析节点度和介数中心性，识别风险的扩散效应和传播程度。结果表明，该方法的知识提取准确率较高（平均F1分数为95.85%），能够实现工程操作风险的定性分析和定量计算。相关研究可以有效增强工程安全管理的可靠性，降低工程风险对供水安全的影响。