## 3 Analysis of Experimental Results
### Analysis of Information Recognition Effectiveness for Secondary Cabinet Terminal Blocks
#### 3.5.3 Analysis of Diagram-to-Physical Matching Results in Substation Secondary Systems  
As previously mentioned, after the recognition model extracts data and merges nodes from both the diagrams and the physical entities of terminal blocks, two separate topological maps are formed for a substation (see Figure 12). By comparing the details of these two maps, the verification of connection lines in the substation's secondary system can be achieved. This section experimentally validates the effectiveness of the topological mapping algorithm proposed in Section 2.4 and compares it with traditional algorithms to demonstrate its superiority. The node embedding and graph embedding networks within the algorithmic model are already detailed in Section 2.4.2. For the DQN network, a four-layer MLP network is chosen to map the graph-embedded information to corresponding scalars, which are then transformed into Q-scores through the RELU activation function. Normally, the training of the model requires a substantial amount of topological map data, and consumes considerable time and computational resources. To mitigate this, pre-training is employed to complete the initial parameter optimization, significantly reducing the training cost of the model. For this purpose, the model is trained on a specialized network based on the graph embedding model trained in [4], significantly reducing the model's convergence time. To validate the effectiveness of the model, this study compares its algorithm with traditional algorithms based on two metrics: the proportion of the largest common subgraph (R) and the algorithm execution time (t/s). The control groups are as follows: (1) the most basic point-to-point data comparison, denoted as N-to-N, which theoretically can fully realize node-to-node relationship comparison but is the most time-consuming; (2) precise MCS algorithms, such as MCSP+RL [18]; and (3) learning-based graph matching models, like GW-QAP [30]. Additionally, to validate the effectiveness of the DQN network designed in this study, a comparative experiment is added where a random number replaces the output value of the DQN network, denoted as DQN-rand. For the aforementioned five groups of models, algorithms are evaluated sequentially using networks of varying scales. The results are shown in Tables 6 and 7. Figure 12: Visualization of topological information in substation secondary screen cabinet terminal block graphical and physical entities. (**a**) Topological Information of the Schematic Diagram. (**b**) Topological Information of the Physical Entity. As seen in Figure 13, when the scale of the nodes is small, all algorithms perform similarly in terms of the R metric. The operational outcomes are all closely aligned with the N-to-N strategy. As the node scale increases, the performance of the DQN-rand, MCSP+RL, and GW-QAP models gradually deteriorates. These three models exhibit weak adaptability to large-scale topological graphs. When the Q-scores of the DQN network are replaced by random numbers, the optimization results cannot be fed back into the network input, causing the model to degrade to the original MCS framework. This further substantiates the effectiveness of the algorithm proposed in this paper. Note: the N-to-N strategy has not been fully executed when the node scale exceeds 50,000. Figure 13 illustrates the time taken to complete different tasks. It is evident that, for the N-to-N strategy, the execution time increases rapidly as the complexity of the network structure grows. When the network scale reaches 50,000, it essentially becomes infeasible to complete the data comparison tasks. For large-scale topological structures, the model proposed in this paper has the shortest running time. This is because the node information embedding model chosen in this paper is the SAGE model, which is well-suited for embedding information in large-scale topological graphs.