毕业生知识库

    国家电力系统运行的可靠性和稳定性对工业生产和人民生活至关重要。但是， 目前部分电网故障诊断工作仍然需要运维工程师进行实地排查，他们的工作经验和知识储备量直接决定了电网故障诊断和维修的质量。因此，实现准确高效的电网故障诊断，缩小人为因素造成的效率差异是当前亟需突破的难点。近年来，人工智能技术与传统工业领域的关联愈发紧密，如果能将人工智能技术应用到电网故障诊断中，解决这一难题将成为可能，对电网领域的发展具有重要意义。

    本文针对基于知识图谱的电网故障辅助诊断系统开展研究工作。运维工程师可以在系统中用自然语言文本输入问题，通过基于知识图谱的推理模型挖掘电网知识的内在联系，获取需要的相关知识和故障分析结果。本文的主要工作如下:

    首先构建了电网故障诊断领域知识图谱。为了贴合实际的电网运维场景，本文综合考虑了多个子领域，提出了融合电网结构和电气故障的知识图谱模式层设计方法，涵盖了电网系统结构、故障类型和解决方案等信息，以此支持对运维工程师的问题推理。

    然后设计了基于知识问答的电网故障辅助诊断模型。为了克服传统问答模型难以完成路径缺失下的隐式推理这一问题，本文设计了消息传递门控网络，将推理路径的结构特征聚合至实体和关系的特征。直接根据问句链接的头实体和关系预测候选答案节点的链接置信度，同时实现了显式和隐式的知识推理。

    最后开发了电网故障辅助诊断系统，集成了电网故障诊断领域知识图谱和基于知识问答的辅助诊断模型。针对辅助诊断模型无法推理动态知识图谱的局限性， 本文提出了融合用户特征的动态问答策略，将运维工程师维护数据库的个人倾向 和操作记录作为辅助诊断模型的参考，进一步提高了本系统的灵活性和应用价值。

    综上所述，电网故障辅助诊断系统是协助运维人员进行故障排查的有力工具。 将知识图谱技术应用到电网故障诊断领域中，可以实现故障知识的高效推理。这 种基于知识图谱的方法可以提供更加精准和高效的问题解决方案，同时也可以实 现知识共享和传递，促进电网故障诊断领域的技术创新和发展。

    The reliability and stability of the national power system operation are crucial to industrial production and people's lives. However, at present, some grid fault diagnosis work still requires on-site investigation by operation and maintenance engineers. Their work experience and knowledge reserves directly determine the quality of grid fault diagnosis and maintenance. Therefore, realizing accurate and efficient grid fault diagnosis and reducing the efficiency difference caused by human factors are the difficulties that need to be broken through urgently. In recent years, artificial intelligence technology has become more and more closely related to traditional industrial fields. If artificial intelligence technology can be applied to power grid fault diagnosis, it will be possible to solve this problem, which is of great significance to the development of the power grid field.

    This thesis conducts research on the auxiliary diagnosis system of power grid faults based on knowledge graph. Operation and maintenance engineers can use natural language text to input questions in the system, and use the reasoning model based on the knowledge graph to mine the internal relationship of power grid knowledge to obtain the required relevant knowledge and fault analysis results. The main work of this thesis is as follows:

    Firstly, a knowledge graph in the field of power grid fault diagnosis is constructed. In order to fit the actual grid operation and maintenance scenario, this thesis comprehensively considers multiple sub-fields, and proposes a knowledge graph model layer design method that integrates grid structure and electrical faults, covering information such as grid system structure, fault types, and solutions. Support problem reasoning for operation and maintenance engineers.

    Then, an auxiliary diagnosis model of power grid faults based on knowledge question answering is designed. In order to overcome the problem that the traditional question answering model is difficult to complete implicit reasoning in the absence of paths, this thesis designs a message passing gating network to aggregate the structural features of reasoning paths into the features of entities and relations. The link confidence of candidate answer nodes is predicted directly based on the head entity and relationship of the question link, and the explicit and implicit knowledge reasoning is realized at the same time.

    Finally, an auxiliary diagnosis system for power grid faults is developed, which integrates the knowledge graph in the field of power grid fault diagnosis and the auxiliary diagnosis model based on knowledge question answering. Aiming at the limitation that the auxiliary diagnosis model cannot reason about the dynamic knowledge graph, this thesis proposes a dynamic question-and-answer strategy that integrates user characteristics, and uses the personal tendency and operation records of the operation and maintenance engineer to maintain the database as a reference for the auxiliary diagnosis model, which further improves the flexibility of the system and application value.

    To sum up, the grid fault auxiliary diagnosis system is a powerful tool to assist operation and maintenance personnel in troubleshooting. Applying knowledge graph technology to the field of power grid fault diagnosis can realize efficient reasoning of fault knowledge. This knowledge graph-based method can provide more accurate and efficient problem solutions, and can also realize knowledge sharing and transfer, and promote technological innovation and development in the field of power grid fault diagnosis.