The Application of the SOFM Neural Network and Internet of Things in Rural Revitalization

To promote rural revitalization under the premise of fully considering the ecological risk factors of land consolidation, this study takes A County in Shaanxi Province as a case study and introduces Self-Organizing Feature Map (SOFM) neural network and Internet of Things (IoT) technology to partition the land. In this study, a comprehensive index system is constructed based on IoT technology, and the relevant factors are quantitatively analyzed from the perspective of land consolidation ecological risk. Then, the land consolidation project area’s attribute and geographic space domains are used as the input of the SOFM neural network to reveal the distribution and influence degree of each factor in the area and determine the zoning pattern of land consolidation in A County. The results show that the rural revitalization land zoning pattern of land consolidation in A County, Shaanxi Province, is divided into four land consolidation areas. Firstly, the priority remediation area soon covers 27 administrative villages with a total area of 28090 hm2, accounting for 32.75%. Secondly, the moderate renovation area is soon classified into 37 administrative villages, with a total area of 15986 hm2, equivalent to 18.55%. In the medium term, the land-saving renovation area covers 39 administrative villages with a total area of 19686 hm2, accounting for 22.75% of the total area. Finally, in the long-term restricted remediation area, 37 administrative villages are divided, with a total area of 22081 hm2, accounting for 25.67% of the total area. These data results provide a quantitative basis for land consolidation planning in this area to achieve the goal of rural land revitalization in different time ranges. This study is significant for implementing land consolidation projects in rural revitalization and provides a valuable reference for developing other similar areas.