基于多模块协同的动物疫病数据综合分析与预警平台(ReEpi)的设计与开发

doi:10.19788/j.issn.2096-6369.000140

Abstract

Abstract:

Given the characteristics of animal disease data, such as multi-source heterogeneity, spatiotemporal correlation, and small-sample imbalance, this study constructs and implements a multi-module collaborative intelligent analysis and early warning platform for animal diseases (ReEpi). Covering the entire process of "data-analysis-early warning-visualization", the platform integrates functional modules including data governance, statistical analysis, epidemiological modeling, molecular evolution analysis, spatial geographic analysis, intelligent diagnosis, and risk early warning. It adopts a reusable and loosely coupled architecture, combined with the Streamlit frontend and Python scientific computing ecosystem, ensuring both usability and scalability. Integrated tests and preliminary applications show that the platform performs well in accuracy, stability, and interactive experience, and can effectively support animal disease analysis and auxiliary decision-making in the context of agricultural big data.

Key words: big data analytics in agriculture, animal infectious disease, epidemiology, spatial analysis, risk early warning, visualization

LIU SiYan, WEI LiLi, WANG JingFei. Design and Development of an Integrated Analysis and Early Warning Platform for Animal Epidemic Data Based on Multi-Module Collaboration[J].Journal of Agricultural Big Data, 2026, 8(2): 174-182.

Figures/Tables 6

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Table 1

Table 2

Assessment results of each module's function and operating status based on AI-assisted evaluation"

模块	稳定性	可用性	说明
数据管理模块	高	高	支持多格式数据读取、列选择与质量统计，异常处理健全，为各分析模块提供稳定数据源。
数据可视化模块	中-高	高	支持Matplotlib/Seaborn/Plotly的多类别图表输出，并加入中文字体自动配置与导出能力；对多图表并行渲染依赖前端资源，建议在大数据量场景下做性能把控。
统计分析模块	高	高	采用标准统计库（scipy、sklearn）完成假设检验、相关分析、聚类等功能，数据加载与列选择流程健全，异常处理与交互式仪表盘完备，适合论文中展示多类统计分析流程。
流行病学分析模块	高	高	所有流程（数据加载、三间分布、风险因子、研究设计分析）均调用真实算法并包含异常处理，运行稳定，输出含汇总与明细表格，满足论文级复现与应用需求。
分子进化分析模块	高	高	所有关键算法（序列比对、系统发育、起源预测、空间聚类）接入真实计算流程；异常处理与数据校验完善，具备实际研究可用性。
风险预警模块	中-高	高	时空趋势预测与风险评估逻辑已动态化，回退策略基于真实统计；模型在多数据条件下可运行，但对大规模数据需要进一步性能评估。
传播动力学模块	中	中-高	实现SIR/SEIR/SIRS常微分方程模拟并集成防控参数，空间模拟使用固定随机种子生成网格；核心模型稳定，若用于发表建议补充实测数据校验与参数灵敏度分析。
智能诊断模块	中	中-高	流程闭环完整，含多模型训练与评估；需确保输入数据质量和特征工程以达到医疗级准确度，建议配合领域专家验证。
地理分析模块	高	高	增加了中间数据缓存与Folium图例统一渲染后，刷新性能显著改善；交互式热力图与聚类图均可用于实地决策支持。

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References 8

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评级等级	平均得分区间	评价说明
高	≥4.0	系统整体表现优良，无重大稳定性或安全性问题，具备较高工程成熟度与实际应用价值
中	3.0-3.9	系统整体表现良好，存在少量不足但无重大隐患，不影响正常功能使用
低	2.0-2.9	系统存在明显短板，部分维度表现较弱，可能出现偶发异常，需进一步优化
差	<2.0	系统存在严重稳定性或安全性问题，难以满足实际应用需求

Design and Development of an Integrated Analysis and Early Warning Platform for Animal Epidemic Data Based on Multi-Module Collaboration

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