基于深度学习的网络攻击检测是对欧几里得数据进行建模,无法学习攻击数据中的结构特征。为此,提出一种基于改进图采样与聚合(graph sample and aggregate,GraphSAGE)的网络攻击检测算法。首先,将攻击数据从平面结构转换为图结构数据。其...基于深度学习的网络攻击检测是对欧几里得数据进行建模,无法学习攻击数据中的结构特征。为此,提出一种基于改进图采样与聚合(graph sample and aggregate,GraphSAGE)的网络攻击检测算法。首先,将攻击数据从平面结构转换为图结构数据。其次,对GraphSAGE算法进行了改进,包括在消息传递阶段融合节点和边的特征,同时在消息聚合过程中考虑不同源节点对目标节点的影响程度,并在边嵌入生成时引入残差学习机制。在两个公开网络攻击数据集上的实验结果表明,在二分类情况下,所提算法的总体性能优于E-GraphSAGE、LSTM、RNN、CNN算法;在多分类情况下,所提算法在大多数攻击类型上的F1值高于对比算法。展开更多
为解决财务人员数字技术应用能力不足、传统财务流程中数据采集质量差导致重复返工、人工数据处理效率低等问题,设计开发了财务共享辅助系统。采用机器人流程自动化(RPA,Robotic Process Automation)和图检索增强生成(Graph RAG,Graph-b...为解决财务人员数字技术应用能力不足、传统财务流程中数据采集质量差导致重复返工、人工数据处理效率低等问题,设计开发了财务共享辅助系统。采用机器人流程自动化(RPA,Robotic Process Automation)和图检索增强生成(Graph RAG,Graph-based Retrieval-Augmented Generation)技术,实现数据填报收集、RPA自动化处理、智能问答等功能,显著提升财务报账效率,为铁路局集团公司财务共享中心的建设提供支撑。展开更多
电力系统的安全稳定运行是保障国家能源安全和经济发展的关键,而这在很大程度上依赖于对电力物联设备故障的准确预测。当前,随着电力物联网技术的发展,大量的数据被采集,但这些数据的潜在价值尚未得到充分挖掘,这在一定程度上限制了故...电力系统的安全稳定运行是保障国家能源安全和经济发展的关键,而这在很大程度上依赖于对电力物联设备故障的准确预测。当前,随着电力物联网技术的发展,大量的数据被采集,但这些数据的潜在价值尚未得到充分挖掘,这在一定程度上限制了故障预测的准确性,影响了电力系统的可靠运行。针对这一问题,该文提出了一种创新的基于GraphSAGE(Graph Sample and Aggregate)算法的电力物联设备故障预测。该方法通过PowerGraph数据集,将电力物联设备故障场景细分为四类,利用GraphSAGE模型的特性,深入学习和分析节点特征与边特征,从而实现对物联设备故障的有效预测。实验结果表明,该方法准确率达到97.5%,相较于其它传统方法,准确率提高了0.39%~6.21%,同时GraphSAGE模型实现了快速训练。该方法为电力物联设备安全稳定运行提供重要决策支持,能够对动态和相互联系的复杂系统进行更精细的分析,并增强电力系统运营部门对潜在干扰的预见和应对能力。展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
文摘基于深度学习的网络攻击检测是对欧几里得数据进行建模,无法学习攻击数据中的结构特征。为此,提出一种基于改进图采样与聚合(graph sample and aggregate,GraphSAGE)的网络攻击检测算法。首先,将攻击数据从平面结构转换为图结构数据。其次,对GraphSAGE算法进行了改进,包括在消息传递阶段融合节点和边的特征,同时在消息聚合过程中考虑不同源节点对目标节点的影响程度,并在边嵌入生成时引入残差学习机制。在两个公开网络攻击数据集上的实验结果表明,在二分类情况下,所提算法的总体性能优于E-GraphSAGE、LSTM、RNN、CNN算法;在多分类情况下,所提算法在大多数攻击类型上的F1值高于对比算法。
文摘电力系统的安全稳定运行是保障国家能源安全和经济发展的关键,而这在很大程度上依赖于对电力物联设备故障的准确预测。当前,随着电力物联网技术的发展,大量的数据被采集,但这些数据的潜在价值尚未得到充分挖掘,这在一定程度上限制了故障预测的准确性,影响了电力系统的可靠运行。针对这一问题,该文提出了一种创新的基于GraphSAGE(Graph Sample and Aggregate)算法的电力物联设备故障预测。该方法通过PowerGraph数据集,将电力物联设备故障场景细分为四类,利用GraphSAGE模型的特性,深入学习和分析节点特征与边特征,从而实现对物联设备故障的有效预测。实验结果表明,该方法准确率达到97.5%,相较于其它传统方法,准确率提高了0.39%~6.21%,同时GraphSAGE模型实现了快速训练。该方法为电力物联设备安全稳定运行提供重要决策支持,能够对动态和相互联系的复杂系统进行更精细的分析,并增强电力系统运营部门对潜在干扰的预见和应对能力。
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
