为应对当前电网应急预案在海量数据查询和现场指导能力上的局限性, 并推动应急预案的数字化建设, 该文提出了一种改进的电网应急预案领域本体模型构建方法。基于多伦多虚拟企业(TOVE)法和骨架法对传统的七步本体构建法进行细化; 采用改进后的方法全面实现了本体模型的目标确定及构建流程; 运用Protégé工具进行可视化展示和实例验证; 将该领域本体模型应用于应急预案的语义网络研究中。实证结果表明：该本体模型不仅能够显著提升应急人员的应急处置与决策效率, 还能有效增强电网应急预案知识的表达能力与数字化建设水平。

Objective: With the continuous expansion of power grid engineering and the rapid enhancement of information technology, the complexity of accident scenarios has increased significantly, leading to an explosive growth in monitoring data. This study aims to address the limitations of current power grid emergency plans in large-scale data querying and on-site guidance, and assist emergency decision-makers in quickly generating response plans, accurately allocating emergency resources, and promote the digitalization of emergency plans. Toward this goal, this study proposes an improved method for constructing an ontology model in power grid emergency planning. Methods: First, the traditional seven-step ontology construction method is refined based on the Toronto virtual enterprise (TOVE) and skeletal methods. In the refinement process, an "application scenario analysis" phase is introduced in the initial step to enhance the relevance of the ontology construction. Additionally, after creating ontology instances, a "qualitative and quantitative analysis" phase is adopted to verify the scientific validity and feasibility of the ontology, thereby improving model quality. Subsequently, the improved method comprehensively implements the goal determination and construction processes of the ontology model. These processes include defining knowledge in the power grid emergency planning domain; evaluating the reuse of existing ontologies; clarifying key concepts from legislation, emergency scenarios, and enterprise planning systems; and establishing class hierarchies and attributes. Next, the Protégé tool is employed for model visualization. For the example of the emergency plan for typhoon disaster events from a provincial power company, a model was constructed comprising 39 ontology categories, 24 relationship categories, and 14 attribute categories, supplemented by 408 entities, 774 relationships, and 334 attributes. Finally, the ontology model is applied to study the semantic network of emergency plans, designing a schema for the knowledge graph of emergency plans for power enterprises based on the resource description framework schema and web ontology language frameworks. The ontology model in the field of power grid emergency planning is visualized using Protégé. The richness and structural integrity of the model are evaluated using a HermiT1.4.3.456 reasoning engine and the ontology quality analysis method. Results: The results indicate that the relationship richness of the model approaches 1, suggesting a rich relationship structure; the attribute richness value exceeds 1, indicating reasonable attribute settings; and the richness of major classes is 1, whereas that of minor classes is 0.9474, close to 1, demonstrating a high utilization rate of classes. Overall, the model exhibits good rationality and practicality. Conclusions: Empirical results demonstrate that this ontology model effectively addresses impracticality issues, usability, and relevance often encountered in emergency plans. It significantly enhances the efficiency of emergency personnel in response and decision-making while also improving the expressiveness and digital construction of knowledge related to power grid emergency planning.