## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling
#### 3.1.2 Task Scheduling Optimization Based on CMMN Algorithm
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **Basic Rendering** & **Occlusion Culling** & **Batch Rendering** & **Both** \\ \hline D0 & 16,419 & 3144 & 41 & 39 \\ D1 & 126,080 & 12,905 & 50 & 41 \\ D2 & Out of memory & 53,876 & 126 & 42 \\ D3 & Out of memory & Out of memory & 801 & 58 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Memory usage (MB). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **Basic Rendering** & **Occlusion Culling** & **Batch Rendering** & **Both** \\ \hline D0 & 724 & 189 & 189 & 113 \\ D1 & 2281 & 444 & 295 & 144 \\ D2 & Out of memory & 2241 & 1080 & 382 \\ D3 & Out of memory & Out of memory & 7325 & 384 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Memory usage (MB). Figure 10: Performance optimization comparison. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **Basic Rendering** & **Occlusion Culling** & **Batch Rendering** & **Both** \\ \hline D0 & 16,406 & 3131 & 16,406 & 3131 \\ D1 & 126,067 & 12,892 & 126,067 & 12,892 \\ D2 & Out of memory & 53,876 & 983,021 & 53,863 \\ D3 & Out of memory & Out of memory & 7,743,278 & 251,885 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Memory usage (MB). Table 5 shows the rendering time results. At the D1 resolution, conventional rendering required 651.6 milliseconds. When occlusion culling was applied, the time dropped to 411 ms, leading to a 36.9% improvement in performance. Incorporating batch processing reduced the time to 568.8 ms, which is a 12.7% performance boost. Applying both strategies simultaneously reduced rendering time to 256.4 ms, marking a 60.6% improvement in performance. The occlusion culling strategy reduces memory usage by eliminating occluded objects from rendering, while the batch rendering strategy minimizes memory pressure by grouping similar objects and sharing textures. Together, these strategies reduce client memory usage by 93.7%, ensuring the successful operation of the D3 high-resolution fusion model. Additionally, occlusion culling decreases the rendered data volume, improving efficiency, while batch rendering reduces CPU-GPU communication and idle GPU time, further boosting performance. The combined strategies cut the rendering load time by 60.6%, enabling the fast loading of the D3 model. ### Case Study

Figure 11 displays the comprehensive application results of the digital exploration three-dimensional visualization analysis system in a gold-copper exploration area. The area covers 4.58 km\({}^{2}\) and exhibits multiple phases of hydrothermal alteration with superimposed features. The main alteration types include (1) silicification, as shown by the purple zone in Figure 11h; (2) alunite alteration; (3) kaolinite alteration; and (4) sericite alteration. Among these, the strongly silicified zone is distributed in a dome shape in the upper part of the orebody. It shows a significant spatial coupling with gold mineralization, which is indicated by the orange zone in Figure 11h. Mineralization characteristics indicate that the gold orebody, marked in orange in Figure 11b, and the copper orebody, marked in green in Figure 11b, occur in vertical zonation. The gold orebody mainly occurs in the oxidation zone above the water table (at elevations between 600 and 640 m), depicted in light blue in Figure 11g, and gradually transitions into a copper sulfide orebody along a northeast-trending extension. Three-dimensional modeling reveals that the orebody exhibits local discontinuities in the X direction, as shown in Figure 11i, and displays a typical gourd-shaped structure overall. This system integrates digital data from 189 exploration drill holes, with rock core data marked in green in Figure 11a and a cumulative drilling length of 99,000 m. Users can obtain real-time information on lithological characteristics, stratigraphic attribution, and mineralization for any drill segment by using an interactive drilling footage visualization module shown in Figure 11c.