# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 2 Constructing a Knowledge Graph for Comprehensive Line Loss Evaluation Management
### Fuzzy DEMATEL Method  
The DEMATEL (Decision-Making Trial and Evaluation Laboratory) method, also known as the Decision-Making Trial and Evaluation Laboratory method, is a system analysis technique that employs graph theory and matrices. It was first proposed by Gabus and Fontela in the United States in 2010. The method was first proposed by [PERSON] and [PERSON] in 2010. The triangular fuzzy numbers introduced in fuzzy set theory serve to enhance the DEMATEL method, circumventing the potential subjectivity of expert input, quantifying the correlation between factors, addressing the ambiguity inherent in the evaluation process, and ensuring the accuracy and reliability of the resulting weightings [24]. By analyzing the logical relationship between each element in the system and the direct influence relationship between the phases, the degree of influence of each factor on other factors and the degree of being influenced is calculated. This allows the cause degree and center degree of the factors to be calculated [25].  
Literature [26] calculated the influence degree \(D_{i}\), the influenced degree \(C_{i}\), and the centrality degree \(M_{i}\) and the cause degree \(R_{i}\) for each element from the comprehensive influence matrix \(Y\). The centrality degree \(M_{i}\) represents the importance of the indicator in the system. The centrality \(M_{i}\) characterizes the importance of the indicator in the system, and the larger the centrality, the more important the indicator is. Finally, from the centrality \(M_{i}\), the subjective weights assigned by the DEMATEL method can be derived:  
\[w_{j}=\frac{M_{i}}{\sum_{i=1}^{n}M_{i}} \tag{9}\]  
where \(w_{j}\) denotes the weight of each indicator.  
Figure 5: Technical roadmap diagram.  
[PERSON] Entropy Weight Method Objective Weighting  
The concept of entropy was first introduced into the field of information theory by [PERSON] and has since been [PERSON] extensively in a range of disciplines, including engineering technology, social economy, and other fields. The entropy weight method is capable of determining the objective weight in accordance with the magnitude of the variability observed in line loss indicators. In general, a smaller information entropy value for an indicator indicates a greater degree of variability, whereby the indicator provides more information and plays a more significant role in the comprehensive evaluation. Consequently, the weight assigned to the indicator is also greater. Conversely, an indicator with a larger information entropy will exhibit a smaller degree of variability in its value. This indicates a reduction in the amount of information provided, which subsequently limits its capacity to contribute to the comprehensive evaluation of online losses and results in a reduction in the assigned weight [27].  
\[e_{j}=-k\sum_{i=1}^{n}p_{ij}\ln\left(p_{ij}\right),j=1,\ldots,m \tag{10}\]  
In the formula, \(e_{j}\) represents the entropy value of the \(j\)th index, and the \(p_{ij}\) sample value accounts for the proportion of the index.  
Where \(n\) denotes the number of indicators, \(e_{j}\) denotes the entropy value of the \(j\)th indicator, and \(p_{ij}\) the weight of the sample value of the indicator.  
\[w_{j}=\frac{1-e_{j}}{\sum_{j=1}^{m}e_{j}},j=1,\ldots,m \tag{11}\]  
where \(w_{j}\) denotes the weight of each indicator.