## 3 System Architecture and Key Technologies
### CMMN-Based Heterogeneous Cloud Resource Integration and Scheduling
#### 3.1.2 Task Scheduling Optimization Based on CMMN Algorithm
reducing memory usage by 93.7% and shortening the rendering time by 60.6%, alleviating the client-side performance burden and ensuring the efficient handling of large datasets. Applied to a gold-copper exploration area, the system integrated 189 drill holes to construct a 3D block model, revealing orebody distribution and key silicification zones. Real-time interactive analysis identified 63.2% siliceous rocks in mineralized zones, enabling precise resource classification and mine planning. This validated the system's ability to enhance decision making in complex geological environments. These results significantly enhance the management of multi-source heterogeneous geological data and improve the visualization and analysis of 3D geological models. However, the system has some limitations that warrant further investigation. In light of these challenges, future research should focus on optimizing ultra-high-resolution models for low-end hardware, automating the semantic and spatial mapping processes in data fusion, and developing dynamic view generation frameworks that support real-time collaborative editing and streaming data integration, thereby extending the system's utility in time-sensitive exploration workflows. Conceptualization, G.C., Y.T. and J.W. ; Methodology, J.W. and G.C. ; Software, J.W., G.C. and Y.T. ; Validation, Y.T., G.L. and X.Z. ; Formal analysis, Y.T., G.C. and J.W. ; Investigation, G.C., X.Z. and J.W. ; Resources, J.W., X.Z. and Y.T. ; Data curation, J.W. and G.C. ; Writing--original draft preparation, J.W. and G.C. ; Writing--review and editing, G.C., Y.T., G.L. and X.Z. ; Visualization, J.W. and G.C. ; Supervision, Y.T., G.L. and X.Z. ; Project administration, Y.T. and G.L. ; Funding acquisition, Y.T., G.L. and X.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by the Major Science and Technology Special Project of Guizhou Provincial Department of Science and Technology entitled \"Big Data-Based Metallogenic Regularity and Efficient Prospecting and Exploration of Strategic Mineral Resources in Western Guizhou\" [2025-2027]; National Key Research and Development Program of China under grant 2023 YF0718000; the State Key Laboratory of Biogeology and Environmental Geology under grant 2021; Science and Technology Strategic Prospecting Project of Guizhou Province under grants [2022]ZD003 and [2022]ZD004; Solid mineral digital exploration innovation talent team under grant [2023]TD002; Central Funds Guiding the Local Science and Technology Development under grant [2021] 4027; National Natural Science Foundation of China under grants 42372345 and U1711267; and Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources under grant KF-2023-08-25. Not applicable. The system under investigation can be accessed at [[https://github.com/zbomLeo](https://github.com/zbomLeo)]([https://github.com/zbomLeo](https://github.com/zbomLeo)) (accessed on 1 March 2025). Due to confidentiality restrictions, the case study dataset is not publicly available. We sincerely thank the Innovative Team (Innovative Team for the digital exploration and intelligent prospecting of mineral resources) for their support in data provision and funding. We would also like to express our gratitude to [PERSON] for his invaluable technical guidance throughout this work. His professional advice and suggestions played a crucial role in the successful progress of the research study. The authors declare no conflicts of interest. ## References

* (1) [PERSON]; [PERSON] Experimental research and progress on intelligent mineral exploration based on big data. _Earth Sci. Front._**2024**, _31_, 350-367. [CrossRef]
* (2) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Digital exploration and digital transformation of the geological and mineral exploration industry. _Guizhou Geol._**2021**, _38_, 119-128.