A task decomposition and scheduling model for power IoT data acquisition with overlapping data efficiency optimization.

To address the challenges of low efficiency and high redundancy in massive data acquisition within the Power Internet of Things (PIoT), existing systems suffer from redundant acquisition and resource waste due to insufficient identification of overlapping regions, while traditional scheduling mechanisms struggle to balance task priorities with dynamic scenario requirements. This paper proposes a data acquisition task decomposition and scheduling method optimized through overlapping data analysis. Initially, hash functions are employed to rapidly identify overlapping regions in target data clusters, with a "hyperlink anchoring" mechanism implemented to eliminate redundant data acquisition. Subsequently, a task decomposition model centered on total cost minimization is formulated, prioritizing the allocation of tasks with maximum overlapping regions to optimize resource distribution strategies. Finally, a multi-dimensional dynamic priority scheduling model is developed, integrating task criticality and temporal characteristics to dynamically adjust execution sequences, ensuring high-value tasks achieve priority completion. Case study results demonstrate that the proposed method improves task efficiency by 18.7% compared to baseline methods, while maintaining operational effectiveness under high-load scenarios.