Multi-scale nested model optimal fitting software for spatial estimation variogram of heavy metals in soil: framework, design and implementation

The optimal fitting of variogram is the key to study the spatial variance law of heavy metals in soil, which can effectively improve the accuracy and reliability of spatial interpolation of heavy metals in soil. In this study, the framework of multi-scale nested model optimal fitting software for heavy metals spatial estimation variogram in soil was designed by using microservice architecture. Six kinds of microservices were designed and implemented by mixed programming of Java and Python. Experiments showed that the system interface is simple and friendly, and could substantially reduce the difficulty of optimal fitting of multi-scale nested model of spatial variogram through visualization and interaction. Moreover, the optimal fitting algorithm of multiscale nested model based on deep learning could effectively improve the accuracy of spatial interpolation, and could provide a good software tool for relevant research in the field of resources and environment.