农业物联网数据安全指标体系构建与治理研究OA

The challenges of agricultural IoT data security have intensified,restricting large-scale applications,and there is a lack of systematic guidance framework for improving security capabilities in the field,fragmented data security risks,and an incomplete research system.Based on this,this article aims to clarify the core direction of agricultural IoT data security governance and provide practical theoretical support and path references for industrial practice.The research focuses on the three-layer architecture of the agricultural Internet of Things and establishes a complete research system through the integration of multiple methods.Firstly,using the literature review method,systematically review the technical architecture,application scenarios,and existing security standards of the agricultural Internet of Things;Secondly,by combining the functional characteristics of each layer architecture and using the multidimensional risk decomposition method,the system identifies the security risk dimensions of the entire lifecycle of data;Finally,a quantitative evaluation system was constructed using the Delphi method.The research path of"risk identification system construction protection design"is formed as a whole,and ultimately five dimensions of data security risks,a quantitative evaluation system,and a four-dimensional collaborative protection system are extracted.This study fills the gap in the field of agricultural Internet of Things with a focus on qualitative and lightweight,architecture and governance.The five major data security risk dimensions proposed provide a clear framework for risk identification.The quantitative evaluation system,which includes five primary indicators and 20 secondary indicators,as well as the"technology management standards talent"collaborative protection system,can directly provide guidance for data security assessment and protection practices.However,the research has certain limitations,such as a narrow pilot scope for the evaluation system.Future research will focus on expanding pilot studies on evaluation systems and exploring intelligent protection models for agricultural IoT data security that integrate with artificial intelligence.