农业物联网数据安全指标体系构建与治理研究

doi:10.19788/j.issn.2096-6369.000129

摘要/Abstract

摘要：

农业物联网数据安全挑战加剧，制约规模化应用，且领域内缺少系统性安全能力提升指导框架、数据安全风险碎片化、研究体系不健全，基于此，本文旨在明确农业物联网数据安全治理核心方向，为产业实践提供切实可行的理论支撑与路径参考。研究围绕农业物联网三层架构，融合多方法建立完整研究体系。首先，结合文献梳理法，系统梳理农业物联网技术架构、应用场景及现有安全标准；其次，结合各层架构功能特性，运用多维度风险拆解法，系统识别数据全生命周期安全风险维度；最后借助德尔菲法构建量化评价体系。整体形成“风险识别—体系构建—防护设计”的研究路径，提炼出五大数据安全风险维度、一套量化评价体系及四维协同防护体系。研究填补了农业物联网领域内“重定性轻量化、重架构轻治理”的空白，所提出的五大数据安全风险维度为风险识别提供了清晰框架，含5项一级指标、20项二级指标的量化评价体系及“技术—管理—标准—人才”协同防护体系可直接为数据安全评估与防护实践提供指导，但研究存在一定局限，如评价体系试点范围窄。未来研究将重点扩大评价体系试点研究，并探索与人工智能融合的农业物联网数据安全智能防护模式。

关键词: 农业物联网, 数据安全, 三层架构, 安全威胁, 指标体系

Abstract:

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.

Key words: agricultural internet of things, data security, three layer architecture, security threat, indicator system

孙雨潇, 李峰, 李斌, 李艳丽, 代峰, 郝志强. 农业物联网数据安全指标体系构建与治理研究[J]. 农业大数据学报, 2026, 8(2): 224-234.

SUN YuXiao, LI Feng, LI Bin, LI YanLi, DAI Feng, HAO ZhiQiang. Research on the Construction and Governance of Data Security Index System for Agricultural Internet of Things[J]. Journal of Agricultural Big Data, 2026, 8(2): 224-234.

图/表 5

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表2

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表3

农业物联网数据安全指标评价体系参考"

基础类别		一级指标		二级指标
类别	权重(%)	指标	权重(%)	指标	权重(%)
基础安全能力	25	设备身份认证	8	设备唯一标识符等	3
				双向认证协议覆盖	3
				设备固件签名验证机制完备性	2
		数据加密保护	9	数据传输层加密率	4
				敏感数据静态加密	3
				密钥管理规范性（如密钥轮换周期、HSM硬件支持）	2
		访问控制	8	基于角色的访问控制策略	3
				多因素认证在管理平台的应用	3
				接口调用权限最小化原则	2
数据传输与存储安全	30	网络通信安全	10	通信协议漏洞（如弱密码、明文传输）检测	4
				边缘节点与云端数据同步的完整性校验	4
				网络流量异常实时监测覆盖	2
		数据完整性	10	哈希算法在数据校验中的应用	3
				区块链技术用于关键数据防篡改覆盖	4
				数据备份与恢复机制完备性	3
		边缘计算安全	10	边缘节点数据本地化处理	4
				边缘云协同任务分配的安全隔离策略完备性	4
				边缘设备固件升级漏洞修复及时	2
隐私保护与合规性	20	用户隐私管理	10	匿名化/假名化技术在个人数据中的应用	4
				数据采集知情同意书签署	4
				《通用数据保护条例》（GDPR）/《中华人民共和国个人信息保护法》等合规性审计通过率	2
		数据生命周期管理	10	数据留存周期合规性	4
				数据共享第三方时的脱敏处理	4
				日志审计记录的完整性与可追溯性	2
物理与操作安全	15	设备物理防护	10	农田边缘设备防拆卸/防篡改设计覆盖	4
				设备部署环境的抗干扰能力	4
				设备丢失后的远程擦除功能启用	2
		人员操作规范	5	农户网络安全培训覆盖	2
				运维人员权限分级管理制度执行率	2
				安全事件响应流程完备性	1
威胁检测与应急响应	10	威胁感知能力	7	入侵检测系统在边缘节点的部署	3
				异常行为告警响应时间	2
				安全情报更新频率	2
		应急恢复能力	3	数据泄露事件平均修复时间（MTTR）	1
				灾难恢复计划测试	1
				安全事件事后复盘与改进措施落实	1
	100		100		100

表3

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应用类型	主要特点
智能监测	通过传感器和摄像头，实时监测农田的温度、湿度、光照、土壤、水质、气象等参数，获取作物的生长情况、病虫害等信息^[5]。数据通过网络传输到云端或移动设备上，供分析和决策。
智能控制	通过设置执行器和设备，根据监测数据和预设规则自动或远程控制灌溉、施肥、喷药采收等操作。
智能预警	通过利用大数据分析和人工智能技术，对监测数据进行挖掘预测，及时发现异常情况和潜在风险，提供预警信息和建议措施。
智能追溯	通过使用二维码、RFID等标识技术，对每个农产品进行唯一标识和记录，从种植、加工、运输到销售等各个环节都可以进行追溯^[6]。

应用场景	主要介绍
智慧农场	通过在农场中安装传感器、摄像头、无线通信设备等物联网设备，实现对农田土壤、作物、气象、环境等因素的实时监测和数据采集，通过数据分析、预测和调整，实现精准农业，提高农业生产效率和质量。
无人机农业	无人机通过搭载传感器和相机，进行农田土壤、作物、气象等因素的高效监测和数据采集，对农田进行精准施肥、病虫害预防等操作，实现农业生产的高效率和精度^[7]。
农业物流管理	农业产品的采摘、运输、储存等环节进行实时监控和管理。在物流设备和农产品包装中安装传感器等物联网设备，实现对农产品的追踪和管理。