Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level progra...Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation's function of urban branch road network.展开更多
大语言模型(large language model,LLM)及其衍生的多模态大模型因其强大的生成能力、泛化能力引发了AI新变革,但存在幻觉问题、可解释性差等不足。知识图谱(knowledge graph,KG)具备推理结果可解释、可增量知识更新等能力,但交互能力较...大语言模型(large language model,LLM)及其衍生的多模态大模型因其强大的生成能力、泛化能力引发了AI新变革,但存在幻觉问题、可解释性差等不足。知识图谱(knowledge graph,KG)具备推理结果可解释、可增量知识更新等能力,但交互能力较差。该文综述了知识图谱与大模型技术的发展历程、关键技术、优势与局限。针对电力数据与业务特点,分析了两者应用于电力领域的主流方法,建立了面向电力领域的知识图谱与大模型相融合的技术架构,重点分析了各应用场景的可行性,并指出了未来面临的挑战和可能的研究方向。展开更多
基金Project(2006CB705507) supported by the National Basic Research and Development Program of ChinaProject(20060533036) supported by the Specialized Research Foundation for the Doctoral Program of Higher Education of China
文摘Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation's function of urban branch road network.
文摘大语言模型(large language model,LLM)及其衍生的多模态大模型因其强大的生成能力、泛化能力引发了AI新变革,但存在幻觉问题、可解释性差等不足。知识图谱(knowledge graph,KG)具备推理结果可解释、可增量知识更新等能力,但交互能力较差。该文综述了知识图谱与大模型技术的发展历程、关键技术、优势与局限。针对电力数据与业务特点,分析了两者应用于电力领域的主流方法,建立了面向电力领域的知识图谱与大模型相融合的技术架构,重点分析了各应用场景的可行性,并指出了未来面临的挑战和可能的研究方向。
