A groundwater function evaluation systems and associated model was applied to Pingtan Island in China, to support future development, use and protection of groundwater. Groundwater function was assigned to resource, ecology and geological environmental functions, and then further sub-divided, based on hydrogeological conditions, development status, planning and Chine policy requirements. At the same time, it is the first time to try to apply the technical requirements for the division of groundwater resources in island areas. According to the planning and utilization of groundwater resources in Pingtan Island, established a second-level system of shallow groundwater function zoning. Then, by comparing the strong and weak relationships among the groundwater resource function, ecology function, and geological environment function, developed a scoring system of the shallow groundwater function zoning. The division of shallow groundwater function zones in Pingtan Island was finally carried out. The zoning result will play an essential role in the future development, utilization, and protection of groundwater in Pingtan Island. At the same time, it provides the first example for the application of groundwater function zoning in island areas and improves the theoretical results of groundwater function zoning.

To address the critical challenge of low diagnostic accuracy in multistate bearing fault diagnosis caused by inefficient discriminative feature extraction under varying operating conditions, this paper proposes a novel Parameter-weighted Viridis Image Encoding (PVIE) method. Unlike conventional image encoding techniques (e.g., GADF, GASF, MTF, RP) that often suffer from high computational complexity and limited feature separability in complex scenarios, PVIE integrates Variational Mode Decomposition (VMD) with a newly designed Parameter-weighted Euler Difference Feature Extraction (PWEDFE) module. This module explicitly enhances the nonlinearity and periodicity of fault signatures, mapping them into lightweight 2D feature images via Viridis Feature Value Mapping (VFVM). Extensive experiments on two benchmark datasets demonstrate that PVIE achieves exceptional diagnostic accuracies of 99.92 % and 99.74 %, respectively. Compared to state-of-the-art encoding methods, PVIE improves average accuracy by 21.06 % to 39.78 % while reducing diagnostic time by 53.3 %, significantly outperforming existing approaches in both accuracy and efficiency. Furthermore, the method exhibits robust performance under strong noise interference and small-sample scenarios. These results confirm that PVIE offers a substantial advancement over current research by providing a more discriminative, lightweight, and robust solution for real-time industrial fault diagnosis.