## 5 Comprehensive Evaluation Example and Numerical Simulation  
To prove the rationality and usability of proposed consistency enhancement strategy, the method proposed in this paper is applied to the key part of this evaluation example--evaluation of two generation TDRSS. There are two reasons to choose the TDRSS as the evaluation object: (1) the demonstration platform based on TDRSS is still in the deployment stage, and it faces the upgrading of the system; (2) the TDRSS's efficiency greatly affects the performance upper limit of the space information network. Therefore, it is obviously a more reasonable choice to evaluate an actual on-orbit system. The main technical elements of the next-generation TDRSS upgrade include laser links, flexible modulation, and delay-tolerant network protocols. Once implemented, the TDRSS's transmission performance and network capacity will be greatly improved.The specific procedure of this evaluation example is as follows. 1. Establish the evaluation goals and build an evaluation model. To simplify the example, this paper only selects a single-level indicator element model. 2. Select relevant element indicators from the comprehensive indicator system. First, enumerate the element-level indicators related to the relay satellite system in the comprehensive indicator system, including network delay (c1), channel support capability (c2), network capacity (c3), resource management and control capability (c4), constellation coverage capability (c5), interconnectivity and interoperability (c6), and platform accuracy (c7). 3. Input the expert group's score for each indicator to construct a judgment matrix. We set the number of experts to four, and the corresponding expert judgment matrix is \(A_{k}\), k = 1 to 4, where each judgment matrix element \(\left\{a_{ij}^{k}=\
icefrac{{c_{i}}}{{c_{j}}}\right\}_{1\leq i,j\leq 7}\)\[A_{1}=\left[ \begin{array}{cccccc}1&1/2&1/8&1/2&1/3&1&2\\ 2&1&1/5&2&1/3&2&1\\ 8&5&1&5&4&3&7\\ 2&1/2&1/5&1&2&1&3\\ 3&3&1/4&1/2&1&3&2\\ 1&1/2&1/3&1&1/3&1&2\\ 1/2&1&1/7&1/3&1/2&1/2&1\\ \end{array} \right],A_{2}=\left[ \begin{array}{cccccc}1&1/3&1/5&2&1/7&1/2&1\\ 3&1&1/2&4&1/2&3&2\\ 5&2&1&3&2&3&4\\ 1/2&1/4&1/3&1&1/2&1/3&1\\ 7&2&1/2&2&1&4&5\\ 2&1/3&1/3&3&1/4&1&3\\ 1&1/2&1/4&1/5&1/3&1\\ \end{array} \right],A_{4}=\left[ \begin{array}{cccccc}1&1/2&1&1/3&1/5&1/2&1\\ 2&1&1&4&1/2&2&4\\ 1&1&1&2&2&3&5\\ 3&1/4&1/2&1&1/3&1&2\\ 5&2&1/2&3&1&2&5\\ 2&1/2&1/3&1&1/2&1&3\\ 1&1/4&1/5&1/2&1/5&1/3&1\\ \end{array} \right]\]
4. In this step, the maximum real eigenvalue and consistency ratio will be calculated, and the numerical validation of consistency improvement strategy in Section 4.1 will be performed. If there is a matrix with a consistency ratio > 0.1, the consistency will be enhanced by the enhancement strategy proposed in Section 4.1. Taking the A3 matrix as an example, the method proposed in this paper is compared with the method in [20; 31]. It can be seen that the method in this paper has a better initial value than the classic eigenvector method [20], which has a faster decline rate than the correction method based on confidence [31], and is not limited by the condition of the matrix element is incomplete. The comparison diagram is shown in Figure 6.  
Analysis of results: The consistency enhancement method previously proposed in [31] is essentially a one-step local algorithm. Its solution is to derive other elements of the judgment matrix by extracting local key information. However, after a certain maximum deviation element is corrected, the algorithm will be unable to continue to optimize due to lack of global information; as shown in the figure, the subsequent iteration process convergence rate is very slow. The method proposed in this paper is a dynamic global algorithm. The updated element output by the algorithm each time is the global information from the judgment matrix. As long as the current error element is updated and corrected, it means that the global information of the judgment matrix has changed.