# A Blockchain Copyright Protection Model Based on Vector Map Unique Identification
## 5 Experiment Results and Analysis  
### Experimental Dataset  
To comprehensively assess the effectiveness of the proposed BCPM-UI model, we selected six shapefile datasets representing vector maps from key urban areas--Shanghai, Beijing, Chengdu, Jiangsu Province, Hangzhou, and Nanjing. These datasets exhibit diverse characteristics in terms of coordinate systems, layer complexities, and geographic feature densities, providing a robust evaluation framework. The experimental setup operates on a Windows 10 platform equipped with an Intel i7-9700 CPU and 16 GB RAM, ensuring reliable execution. The unique identification construction was implemented using MATLAB R2023a, leveraging robust computational and visualization capabilities. The blockchain infrastructure employed was the Ant Open Alliance Chain, a consortium blockchain platform that supports high throughput and secure data recording. Integration with MATLAB involved the generation of unique identifiers, which were exported and recorded in the blockchain through API calls facilitated by Python scripts. The development of blockchain smart contracts was carried out within the Cloud IDE provided by Ant Open Alliance Chain. Solidity 0.8.18 was used for contract design and deployment, ensuring compatibility with the platform's virtual machine. The Cloud IDE's integrated environment streamlined the process, from contract development and testing to deployment. This setup ensures reproducibility, as all tools and configurations are well documented and accessible.  
The data in the proposed model were utilized in multiple critical stages. This included constructing unique identifiers based on the geometric and topological characteristics of vector maps, embedding watermark information into the datasets, and recording these identifiers and watermarks on the blockchain. The dataset's diverse attributes allowed us to test the model's adaptability and robustness across different map structures and complexities. Furthermore, the experimental datasets were used to evaluate the integrity and resilience of watermark extraction and identifier matching under various levels of distortion or modification, ensuring comprehensive performance validation.  
In evaluating the adaptability of the model to diverse and complex vector map datasets, this paper selected datasets with varying numbers of layers, geographic feature counts, and coordinate systems, as shown in Figure 7. Table 1 provides detailed information on the experimental datasets, including the vector map names, coordinate systems, layer numbers, and geographic feature counts. Furthermore, to optimize the practical application of watermark information, this paper utilized meaningful binary information, sized at 64 \(\times\) 64, as watermark data. This ensures substantive watermark information while providing a benchmark closer to real-world applications for subsequent model evaluations, as depicted in Figure 8. Through systematic dataset selection and watermark information settings, the relevance of the model's performance can be more accurately tested, thereby enhancing the practicality and generalizability of the research.