# Knowledge-Graph-Based Integrated Line Loss Evaluation Management System
## 5 Construction of a Mapping of Management Cases for Comprehensive Line Loss Evaluation
### Data Analysis and Experimental Platform  
There are as many as 3.05 million local municipal users, and the traditional customer power consumption accurate portrait and collection management is a very difficult and long cycle. This paper will be based on the basic data from 2014 to 2017 in Nanning City, using the combination of DEMATEL-Entropy Weight-TOPSIS evaluation method to carry out empirical research on the whole process of line loss management evaluation system. Based on the above subjective and objective combination of weight determination method, this paper will be in accordance with the scientific, objective, and reasonable evaluation ideas, on top of the analysis of specific data on the basis of the expert assignment of weights, to ensure that the design of the indicator system in the determination of the weight of the indicators is scientific, the weight of the indicator data is objective and reasonable, so that the subjective evaluation method of the expert assignment of the weight obtains \(W\), \(W_{i}\), the objective evaluation method of the weight \(W\), \(W_{i}^{\prime}\), the weights obtained. According to the weight ratios \(W\):\(W\) = 1:1 and \(W_{i}\):\(W_{i}^{\prime}\) = 1:1, the weights of the evaluation indicators in the evaluation indicator system are calculated again. Ablation Experiment Based on DEMATEL-Entropy Weight-TOPSIS Whole Process Combination Evaluation Method  
In order to ascertain the veracity of each component of the prediction model DEMATEL-Entropy Weight-TOPSIS proposed in this paper, ablation experiments have been conducted on this model. Two ablation experiments are conducted, the first of which removes the DEMATEL structure, while the second removes the Entropy Weight. This paper participates in the ablation prediction comparison and evaluation methods, namely the DEMATEL-TOPSIS evaluation and the Entropy Weight-TOPSIS evaluation. These evaluation methods retain the distance between each scheme and the ideal solution in order to compare the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ingredients** & \multicolumn{3}{c}{**Explanation of Total Variance**} \\  & **Amount to** & **Percentage of Variance** & **Cumulative \%** \\ \hline Main variable capacity-to-load ratio & 3.858 & 10.928 & 10.928 \\ The pass rate of reactive power configuration of substation & 2.933 & 8.426 & 19.354 \\ Unplanned outage rate of distribution lines & 2.854 & 7.712 & 27.066 \\ Public change outage rate & 2.448 & 6.617 & 33.683 \\ The proportion of old low-voltage energy meters & 2.130 & 5.758 & 39.441 \\ The proportion of automatic meter reading & 1.936 & 5.232 & 44.673 \\ Data acquisition integrity rate of four types of terminals & 1.870 & 5.053 & 49.726 \\ The rate of emergency repair orders for hundreds of households & 1.651 & 4.461 & 54.187 \\ Electricity sales growth rate & 1.550 & 4.189 & 58.376 \\ The success rate of automatic execution of fee control & 1.346 & 3.637 & 62.013 \\ Power factor pass rate & 1.174 & 3.173 & 65.186 \\ Availability rate of substation reactive power compensation device & 1.167 & 3.155 & 68.341 \\ Energy-saving main variable ratio & 1.062 & 2.870 & 71.211 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Eigenvalues and variance contribution of the principal component analysis method. advantages and disadvantages of the scheme. Additionally, they facilitate a horizontal comparison of the analysis, allowing for the impact of each line loss indicator on the enterprise to be assessed. The incorporation of entropy weighting in the ablation experiment serves to enhance the limitations inherent to the conventional human-subjective assignment of the TOPSIS method. Concurrently, it circumvents the potential distortion of the evaluation outcomes resulting from the influence of numerous human variables. The TOPSIS method does not necessitate a vast number of evaluation samples. It is capable of comprehensively and objectively reflecting the advantages and disadvantages of the various evaluation schemes through the information provided by the original data.