## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling
#### 3.1.2 Task Scheduling Optimization Based on CMMN Algorithm
Each identified file is processed individually, ensuring that its coordinates are transformed to align with the coordinate system of the fusion model. Next, the file data are segmented based on the block model's spatial resolution, and each block's attribute values are calculated. If a block lacks attribute values or the file's priority is higher than the existing data, the block's attributes are updated. This process effectively integrates structured and semi-structured data into the block model. The geographic name mapping repository associates it with the block model's spatial locations for unstructured data, which lack explicit spatial information. This association enables quick access to relevant unstructured data files through the blocks, facilitating the efficient retrieval of files related to specific blocks within the fusion model. #### 3.2.2 Modular Approach for Dynamic Data View Generation

Data views are crucial to data presentation, decision making, analysis, and collaboration in digital exploration. They help exploration personnel efficiently understand data, identify resources and risks, and make timely decisions. Due to the large volume of geological data and the complexity of exploration tasks, dynamic views are necessary to present information based on work focus and data format, supporting interaction. Figure 4 shows the proposed design method, which separates view components into data, templates, and interactive tools. Data include raw, statistical, fused, and real-time interactive data, reflecting resource distribution, geological correlations, and real-time feedback. Templates display data by using charts, images, videos, and geological-specific formats. Interactive tools enable flexible functions, such as triggering pop-ups or controlling view displays, meeting diverse interactive needs. Figure 3: Data fusion workflow. ### Rendering Optimization Strategy for 3D Visual Analytics

Rendering efficiency and resource consumption are crucial to large-scale data fusion for 3D visual analytics. Effective optimization strategies, such as occlusion culling and batch rendering techniques, must be employed to improve rendering performance and reduce hardware strain. #### 3.3.1 Occlusion Culling Strategy

In integrated model rendering, many objects are fully occluded, consuming significant computational resources and memory despite not being visible. Therefore, we propose a two-stage occlusion culling strategy (Figure 5) that reduces the number of rendered objects without affecting visualization quality, improving efficiency, and reducing hardware strain. In the preprocessing stage, occlusion culling computations are reduced to enhance rendering efficiency. The fused model is classified into discrete and continuous attributes, with discrete attributes (e.g., strata) categorized by property values and continuous attributes (e.g., Ag content) by intervals. Initial occlusion culling generates a shell model with only exterior data stored on the cloud for real-time rendering. During rendering, the shell model is used as the data source, filtered by the display range. Secondary occlusion culling is performed

Figure 4: Modular design-based method for dynamic data view generation. Figure 5: Two-stage occlusion culling strategy. (Figure 6) to exclude fully enclosed objects from rendering. Then, the data are submitted for final rendering. #### 3.3.2 Batch Rendering Strategy

Before rendering, the CPU sets the rendering states and adds operations to a queue, notifying the GPU to execute the rendering process (Draw Call). Both excessive and insufficient Draw Calls can reduce efficiency. To optimize, a batch rendering strategy (Figure 7) is used to group similar objects, merging Draw Calls and reducing memory usage by sharing textures, improving efficiency, and reducing hardware load. Let us suppose that each object in the fused model is processed with a separate Draw Call, as illustrated in Figure 7a.