As the foundation of the national economy, agriculture is a high-risk, weak industry. Affected by many factors, agricultural production is subject to catastrophe risks from time to time. Agricultural production is mainly faced with two major threats, natural disaster risk and market risk. As an effective risk management tool, the production and promotion of agricultural insurance have played an essential role in guaranteeing the development of the agricultural industry in some developed countries and major agricultural countries in the world. This article combines the Internet of Things and Markov model for agricultural insurance risk management. First, we combine the structure of the Internet of Things and select relevant statistical data. Then, we build a panel data system, starting from two perspectives in different regions and analyze agricultural insurance’s current development and characteristics at each stage. In addition, we use the Markov model to build a panel data model to explore the specific impact mechanisms deeply. We also study the effects of disaster risk levels in different regions on the development of agricultural insurance. After simulation verification, we believe that this model can effectively promote the balanced regional development of agricultural insurance.

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基于马尔科夫模型的物联网在农业保险和风险管理中的应用

农业作为国民经济的基础，是一个高风险、薄弱的行业。受多种因素影响，农业生产不时面临巨灾风险。农业生产主要面临两大威胁，自然灾害风险和市场风险。作为一种有效的风险管理工具，农业保险的产生和推广在保障部分发达国家和世界主要农业国家农业发展方面发挥了重要作用。本文结合物联网和马尔可夫模型进行农业保险风险管理。首先，我们结合物联网的结构，选取相关的统计数据。然后，我们建立一个面板数据系统，从不同地区的两个角度出发，分析农业保险的发展现状和各个阶段的特点。此外，我们使用马尔可夫模型构建面板数据模型来深入探索具体的影响机制。我们还研究了不同地区灾害风险水平对农业保险发展的影响。经过仿真验证，我们认为该模型能够有效促进农业保险的区域均衡发展。