## 2 Current Works
### Evaluation Method Research  
As the most representative evaluation method in the field of comprehensive evaluation, the AHP was first proposed by Dr. [PERSON] in the 1970s. Because the consistency of the expert score matrix (pairwise comparison judgment matrix) is an important basis for measuring the credibility of the evaluation results, it has been the main research focus of AHP. Based on Frobenius-Perron theorem, [PERSON] first proposed a consistency enhancement algorithm for judgment comparison matrix, which replaces the largest error element by the combination of the main feature vector elements [19]. Later, Dr. [PERSON] analyzed the influence of the small disturbance on the consistency of the judgment matrix from the matrix principle in [20], and calculated the partial differential matrix and error matrix corresponding to the nearly consistent matrix. Furthermore, an improved judgment matrix correction method is given based on [PERSON]'s algorithm. Aiming at the problem of missing elements in the judgment matrix in emergency decision-making scenarios, the authors of [21] extended the geometric mean-induced deviation matrix to calculate the missing elements and improve the consistency ratio of the judgment matrix at the same time. The missing elements given by this method can also be used to correct the error elements. The authors of [22] presented a theoretical framework and a procedure for revising the judgments and improving the inconsistency of an AHP pairwise comparison matrix, when the Row Geometric Mean (RGM) is used as the prioritization procedure and the Geometric Consistency Index (GCI) is the inconsistency measure. With the application of AHP in group decision-making and evaluation, not only must the issue of consistency deviation of the judgment matrix must be considered at this time, but also the judgment matrix given by each expert must be effectively aggregated. In the AHP group decision-making (AHP-GDM), the aggregation of individual judgments (AIJ) and the aggregation of individual priorities (AIP) are divided according to the order of aggregation. Among them, AIJ first aggregates the judgment matrix to obtain a comprehensive judgment matrix, and then obtains the feature vector of the comprehensive judgment matrix. Moreover, AIP is to directly aggregate the eigenvalues and eigenvectors of each judgment matrix [23]. The authors of [24], based on the aggregation order of AIJ, proposed a judgment matrix aggregation method based on Hadamard product. In addition, based on the geometric mean method, the authors of [25] created a new decision tool called the Consistency Consensus Matrix (CCM) for consensus building in AHP-group decision making. The authors of [26] presented an extension of the CCM, the precise consensus consistency matrix (PCCM), which can provide more informed and participative GDM and offers more accurate estimations for the group's priorities. The authors of [27] proposed an iterative algorithm for improving the compatibility of the PCCM, gave a detailed explanation of the process followed to solve the optimization problem, for the consideration of different weights for the decision makers in the calculation of the PCCM. To deal with different levels of consistency when there are a number of decision-makers, the authors of [28] presented some of the methods for defining the individual weights of decision makers in group AHP decision making. The authors of [29] outlined a new approach to aid the consensus decision making process, for which two heuristic algorithms are developed. The first algorithm is designed to assist the decision maker in achieving a predefined consistency level, and the second is designed to achieve consensus while controlling the individual consistency level. Several classical numerical examples are compared to validate the effectiveness of the proposed approach. The aggregation coefficient of those methods only considers the authority of experts, and it cannot fully reflect the impact of the current evaluation error on the final aggregation result.