A Container-Orchestrated Parallel Processing Framework for Efficient Geological Environment Data Analytics

Efficient processing and sharing of geological environmental data are crucial for sustainable development and informed decision-making. However, current analysis methods struggle with low efficiency and resource utilization, especially in complex computational tasks. This paper proposes a parallel processing framework based on container orchestration that systematically improves the efficiency of geological environment data analysis by integrating container technology and complex task processing optimization strategies. Leveraging containerized processing, we established a standardized packaging and deployment mechanism for geological environmental data analysis algorithms, enabling flexible encapsulating and management of multiple models. In addition, we proposed a complex task decomposition method for pipeline parallelism and realized multicontainer collaborative geological environment data processing in a distributed environment based on container orchestration. For enhancing the efficiency of complex task processing purposes, this paper proposed a task scheduling optimization strategy based on the dynamic merging of directed acyclic graph, which improves resource utilization and processing speed through task merging. Experimental results demonstrate that the proposed framework enhances processing efficiency by over 50% in typical geological environmental data analysis scenarios, while improving resource utilization by $\mathbf{4 8 \% - 6 9 \%}$. It exhibits strong reliability and scalability, offering technical support for intelligent analysis and service sharing of geological environmental data.