# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 1 Introduction
In a notable contribution to the field, literature [4] has developed a comprehensive knowledge graph that integrates a vast array of data, topological structures, components, and customer metadata related to the power grid system. The following examples demonstrate the utilization of GridKG for the analysis of the power grid, including the identification of electrical paths and the localization of switches and protective equipment sequences under specific conditions. However, the limitation is that data collection for distribution networks are often singular and subjective, that relevant data are insufficiently interpreted, and that new data are not adequately characterized. Literature [5] proposes a comprehensive framework for quantifying and modeling the resilience of power systems, with a particular focus on the impact of natural disaster events on the system. The authors put forth a novel systematic resilience matrix method based on the axiomatic design concept for the numerical representation of large flexible engineering systems. Nevertheless, the algorithm proposed in the paper may be computationally expensive when  
including the initial investment, operational costs, and potential savings. Concurrently, distribution systems in disparate regions may exhibit disparate design standards, operating environments, and fault patterns, necessitating adjustments to the methods delineated in the paper to align with the particular circumstances of each region. With regard to the aforementioned comprehensive evaluation methods, it is evident that the application of automated fault management in power grids or the introduction of a comprehensive framework, comprising four steps (data requirements, disaster modelling, line status and system topology determination, and resilience index evaluation), to quantify and model the resilience of power systems, the selection of indicators is either one-sided, without exploring the characteristics of line loss, focuses on the analysis of distribution network line loss faults, or focuses on data analysis on the transformer side and the user side, lacking consideration of the dimensions of line loss indicators and the extraction of knowledge from different structured data. At present, there is a paucity of comprehensive evaluations of the entirety of the line loss process within the field of power grids. In these papers [7; 8; 9], the development of smart grid technology necessitates a combination of computer science, artificial intelligence, and knowledge of specific application fields. The processing of data through neural networks or enhanced recommendation systems is contingent upon the analysis and processing of a substantial quantity of data. As a consequence of the process of urbanization and the aging of power grid facilities, coupled with the irrationality of planning, there is an urgent need to carry out a comprehensive and scientific evaluation of the power grid. The objective of such evaluations is to identify the weak links in the power grid system, thereby providing precise targets and direction guidance for the upgrade, transformation, and planning of the power grid. In light of the accelerated advancement of artificial intelligence technology, the knowledge graph (Knowledge Graph, KG), a nascent cognitive computing methodology, has commenced its deployment in the domain of electric power. This method effectively addresses the complex knowledge structure and large-scale, multi-source, heterogeneous data involved in the power grid evaluation process, while integrating the professional knowledge and experience of domain experts and technical personnel. Accordingly, this paper proposes that the lean management of line loss in electric power enterprises be taken as the point of departure, with the objective of integrating data-driven and artificial intelligence technology to construct a line loss management knowledge graph and integrating multi-source data through the knowledge graph with a view to reducing data redundancy.