## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling
#### 3.1.2 Task Scheduling Optimization Based on CMMN Algorithm
The lithology pie chart analysis module, as shown in Figure 11e, provides a quantitative characterization of the drill hole lithological composition, revealing that silicocus rocks account for 63.2% within the mineralized zone, which serves as a basis for identifying key prospecting marker layers. Based on the kriging geostatistical method and using the reference point (X = 39,439,281.81; Y = 2,786,910.52), a three-dimensional block model was constructed by dividing the volume into standardized blocks measuring 12 m \(\times\) 12 m \(\times\) 12 m, totaling \begin{table}
\begin{tabular}{l c c c c} \hline
**Datasets** & **Basic Rendering** & **Occlusion Culling** & **Batch Rendering** & **Both** \\ \hline D0 & 75.5 & 61.7 & 80.3 & 58.26 \\ D1 & 651.6 & 563.8 & 568.8 & 388.3 \\ D2 & Out of memory & 2969.7 & 1585.6 & 3148.1 \\ D3 & Out of memory & Out of memory & 44,961 & 25,753.8 \\ \hline \end{tabular}
\end{table} 
Table 5: Memory usage (MB). 271,568 blocks. The modeling range extends from the bottom boundary of the oxidation zone to an elevation of \(-200\) m, spanning 1584 m along the 50\({}^{\circ}\) section and 1584 m along the 140\({}^{\circ}\) longitudinal section. As shown in Figure 11d, the final gold-grade block model indicates that the orebody extends in a northwest-southeast direction with a three-dimensional double-expanded gourd shape. It has a strike length of 1900 m, a width of 900 m, a vertical extension of 940 m, and a maximum actual thickness of 420 m, with mineralization elevations ranging from 592 to 1120 m. Through grade-tonnage association analysis, as illustrated in Figure 11f, the system automatically generates a resource reserve classification report based on industrial criteria, clearly identifying the proportion and spatial distribution of economically mineable reserves. This provides a precise geological basis for designing mine development systems and optimizing mining sequences. ## 5 Conclusions

This study presents and implements a big data-driven 3D visualization analysis system for promoting regional-scale digital geological exploration, addressing key challenges in digital exploration, including data silos, difficulties in data integration, and inefficiencies in visualization analysis. The system optimizes cross-cloud platform resource allocation by adopting an enhanced CMMN-based method for integrating and scheduling heterogeneous cloud resources. This results in improved task execution efficiency and increased cloud resource utilization. Experimental results indicate that the modified CMMN algorithm reduces cloud completion time by an average of 2.37%. In comparison, cloud resource utilization rises to 92.58%, demonstrating the method's effectiveness in task scheduling and resource allocation. Using a block model-based dynamic aggregation approach, the system integrates structured, semi-structured, and unstructured geological data, generating different data views as needed. This approach improves the accuracy and timeliness of data integration. The application of intelligent data matching and dynamic aggregation techniques enhances the efficiency of geological data integration, particularly in multi-source heterogeneous data. Additionally, occlusion culling and batch rendering techniques were implemented to optimize the rendering performance of large-scale geological models,

Figure 11: Dynamic data view interaction interface: (**a**) Drillhole distribution map; (**b**) Gold (orange) and copper (green) orebody structure; (**c**) Drillhole depth chart; (**d**) Predicted gold ore grade block model; (**e**) Drillhole lithography pie chart; (**f**) Gold ore grade-tonnage estimation table; (**g**) Relationship of oxidation zone (light blue), reduction zone (dark blue), and orebody (orange); (**h**) Relationship of orebody (orange) and alteration zones (other colors); (**i**) Drillhole distribution in different directions, reflecting orebody distribution.