FSOSM: An Operational Knowledge Empowered Scenario Model for the Intelligent Farmland Supervision

The automation of extracting targeted decision-support information is a key task for achieving intelligent agricultural management. Essentially, this involves structurally representing agricultural operations based on knowledge, unified modeling and relational management of elements such as natural resources, human–land relationships, and spatiotemporal data. However, the traditional farmland supervision systems based on relational and object-oriented databases struggle to effectively integrate, model, and apply operational knowledge such as project requirements, work experience, policies, and regulations. This limits their application efficiency and automation level. Therefore, this paper proposes a modeling method for Farmland Supervision Operations Scenario Model (FSOSM) based on structured operational knowledge. First, by analyzing the elements, structure, and functions of farmland supervision business scenario, the paper abstracts “natural resources—human society—spatiotemporal data” into 8 categories of scenario elements and 22 types of multidimensional semantic relationships. Next, the operational knowledge is structured and integrated into various modeling steps, including scenario element extraction, association, expression, and application, thereby enhancing the model’s intelligent service capability. Finally, the model is applied in practice through visualization and service applications using the “Farmland Non-Grain Conversion Supervision Operation Scenario of Guangdong Province, China” as a case study. The model’s practicality and superiority are demonstrated by comparing the processing flows and effects of this model and traditional farmland management systems in terms of efficiency, automation level, knowledge service capability, and versatility.