## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling
#### 3.1.2 Task Scheduling Optimization Based on CMMN Algorithm
In that case, it leads to excessive Draw Calls, which in turn consume significant CPU resources and cause the GPU to wait for subsequent render commands, reducing overall efficiency idly. On the other hand, grouping all similar objects into a single Draw Call, as shown in Figure 7b, effectively reduces the number of Draw Calls. However, this approach may not fully exploit the GPU's parallel processing capabilities, and the low communication frequency between the CPU and GPU can result in suboptimal synchronization, further decreasing rendering performance. A hybrid approach is employed to mitigate these issues, as depicted in Figure 7c. By determining an optimal number of objects to merge per Draw Call based on the size of the fused model, this strategy balances the two previous methods, ultimately optimizing rendering efficiency. ## 4 Experiments and Results

Several experiments were designed and conducted to evaluate the effectiveness of key technologies and validate the system's feasibility. Section 4.1 presents a simulation experiment of the CMMN optimization algorithm, where the results before and after optimization demonstrate the effectiveness of the measures in improving task efficiency and optimizing load balancing. Section 4.2 showcases the dynamic data view generation process through examples, validating the feasibility of the modular design-based method for dynamic data view generation. Section 4.3 focuses on experiments with 3D model

Figure 6: Shell block identification diagram: (**a**) Case with 5 neighbor blocks; (**b,c**) Two cases with 4 neighbor blocks; (**d,e**) Two cases with 3 neighbor blocks; (**f,g**) Two cases with 2 neighbor blocks; (**h**) Case with 1 neighbor block. Figure 7: Batch rendering strategy: (**a**) Rendering without batch merging; (**b**) Over-merged batching before rendering; (**c**) Grouping first, then proper batching before rendering. rendering, verifying the advantages of the proposed strategy in reducing client memory load and improving rendering efficiency. The experimental environment consisted of an Intel (R) Core (TM) i7-9700 CPU @3.00 GHz (eight cores), 32 GB of RAM, and an NVIDIA Quadro P620 GPU running on Windows 10. The code was written in Java, using JDK 1.8 as the development environment, and the browser used was Microsoft Edge version 130.0.2849.52. The experimental data are derived from raw records accumulated over years of digital exploration in Guizhou, China, including drilling data, geophysical data, geochemical data, remote sensing images, geological map databases, and multiscale 3D geological models. In Section 4.1, task data are randomly generated to simulate a heterogeneous cloud environment with cloud preparation times set between 1 and 10 and task execution times between 5 and 100; moreover, 20% of the tasks have no predecessors, with a maximum of six predecessors. By gradually increasing the number of tasks and cloud nodes (e.g., from \"100 \(\times\) 4\" to \"1000 \(\times\) 40\"), 100 repeated datasets were generated to evaluate the efficiency and resource utilization of the improved CMMN algorithm. Section 4.2 configures view templates using drilling cores, statistical indicators, and real-time interactive data, and user interaction experiments validate the practicality of the modular design. In Section 4.3, a mining area 3D geological structure model is used to generate block models of different resolutions (D0-D3) through spatial partitioning and interpolation; data volumes see a 7-fold increase from D1 to D3, and tests on memory usage, rendering object count, Draw Calls, and rendering time verify the effectiveness of the occlusion culling and batch rendering strategies. Section 4.4 integrates 189 boreholes (with a cumulative drilling length of 99,000 m) and a 3D block model comprising 271,568 blocks of 12 m\({}^{3}\) each, using kriging interpolation to analyze the spatial distribution and resource reserves of the gold-copper deposit, thereby confirming the system's comprehensive capability in complex geological scenarios.