# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 5 Experiment Results and Analysis
### Experiment
#### 5.3.5 Computational Complexity Analysis  
The computational complexity of the proposed BCPM-UI model is a critical aspect of its evaluation, particularly when processing vector maps with intricate structures and large numbers of elements. This section examines the model's performance through an analysis of copyright registration and verification times across six datasets, as summarized in Table 7.  
The experimental results reveal that the time required for registration and verification tasks increases with the complexity of the vector map, as indicated by the number of layers and features. For relatively simple datasets, such as the Shanghai dataset, with 6214 features distributed across one point layer, one line layer, and one polygon layer, the registration and verification times are 66.9 s and 65.7 s, respectively. In contrast, for the more complex Nanjing dataset, which contains 10 point layers, 6 line layers, and 3 polygon layers encompassing 53,660 features, these tasks take 188.0 s and 180.9 s, respectively.  
The scalability of the model is further evidenced by the near-linear growth of processing times with the increase in the number of features. This suggests that the proposed algorithm is capable of accommodating datasets of varying sizes and complexities without significant performance degradation. Nevertheless, further efforts to optimize the model are warranted. Future work will explore the use of parallel processing techniques to accelerate feature extraction and watermark embedding.  
To further clarify the distinctions between the proposed method (BCPM-UI) and existing approaches, Table 8 provides a detailed comparison in terms of blockchain storage capacity and the robustness of feature identifiers against various attack scenarios. As shown in the table, the proposed model achieves minimal blockchain storage usage while exhibiting robust resistance to geometric attacks, feature addition/deletion, layer addition/deletion, and cropping and merging attacks. In contrast, [PERSON]'s blockchain-based model incurs significant storage requirements due to the lack of an off-chain mechanism for data management. Among the identifier construction methods, [PERSON], [PERSON], and [PERSON]'s approaches demonstrate resilience to geometric attacks, but only BCPM-UI effectively addresses the challenges posed by feature and layer modifications. These results underscore  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name of Vector Map**} & \multicolumn{3}{c}{**Quantity of Map Layers**} & **Quantity of** & **Registration** & **Verification** \\ \cline{2-6}  & **Point Layer** & **Line Layer** & **Polygon Layer** & **Features** & **Time (s)** & **Time (s)** \\ \hline Shanghai dataset & 1 & 1 & 1 & 6214 & 66.9 & 65.7 \\ Beijing dataset & 2 & 1 & 1 & 16,343 & 80.4 & 80.2 \\ Chengdu dataset & 3 & 1 & 1 & 18,769 & 101.7 & 105.1 \\ Jiangsu dataset & 2 & 2 & 1 & 22,168 & 112.4 & 108.0 \\ Hangzhou dataset & 9 & 4 & 2 & 64,810 & 166.0 & 157.6 \\ Nanjing dataset & 10 & 6 & 3 & 53,660 & 188.0 & 180.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results of efficiency comparison experiments.  
the comprehensive adaptability and efficiency of the proposed model for real-world vector map copyright protection.