## 1 Introduction  
Mineral resources are a critical material foundation for social development, and the exploration and development of these resources directly impact the national economy, people's livelihoods, and national security [1]. In the digital exploration process, Guizhou Province has accumulated multi-source, multi-type, and heterogeneous geological data and has established multiple database types [2, 3]. However, data fragmentation, multi-source heterogeneity, and complex integration remain prominent, making it difficult to integrate and visualize geological information effectively [4]. These challenges have become a key bottleneck in the advancement of digital exploration. In recent years, with the rapid development of geological exploration technology, digital and intelligent methods have gradually become essential driving forces for transforming and upgrading the geoscience industry [5; 6; 7]. Geological big data technology provides new approaches to address the issues of data fragmentation and complex integration [8]. Integrating and visualizing multi-source data have significantly improved geological information expression and utilization efficiency, offering more accurate decision support for resource evaluation, mining planning, and environmental protection [9]. For example, [PERSON] et al. (2015) [10] compared three voxel models from the Netherlands Geological Survey. They found that the finest GeoTOP model better delineates geological features such as river channels and accurately predicts extractability. Their findings emphasize the importance of matching model scale with practical application needs. [PERSON] et al. (2025) [11] developed a Geographic Data Modeling and Management System (GEM2S) that features interactive query functions, introduces new criteria for data evaluation, and integrates geophysical data to enhance the quality of geological models. However, regional-scale geological data are vast, high-dimensional, and complexly interrelated. Existing systems often focus on single data types or localized analysis, making it challenging to handle multiscale spatial analysis, data aggregation, and multi-theme attribute expression and are unable to provide a unified, comprehensive, integrated data view [12; 13]. This system faces three challenges in practical application: (1) Various units have established independent data and migrated data to the available managed geological big data. However, differences in storage, formats, and interfaces among cloud platform architectures have led to a lack of effective interoperability and sharing mechanisms, failing to resolve the issue of information silos [14]. This severely limits cross-departmental and cross-domain collaboration and hinders the efficient utilization and sharing of data resources [15]. (2) Geological data lack unified standards in format, semantics, and spatial coordinate systems, requiring tedious conversion and reconstruction for data from different sources [13]. This increases the complexity of data fusion and analysis, impacting the efficiency and accuracy of data processing. (3) Regional-scale geological data are vast and densely distributed. Traditional rendering techniques in 3D environments often result in unnecessary rendering, leading to wasted computational resources and delayed interactive responses, severely affecting the efficiency of visual analysis and user experience [16]. To address the above challenges, a big data-driven 3D visual analysis system for promoting regional-scale digital geological exploration is proposed. First, to tackle the issues of resource isolation and task scheduling complexity in heterogeneous cloud environments during regional-scale digital exploration, a strategy for the unified management and adaptive scheduling of heterogeneous clouds is introduced. This strategy eliminates isolation among cloud platforms by constructing a unified standard cloud API and user management, resource management, and monitoring modules.