Journal of Agricultural Big Data ›› 2025, Vol. 7 ›› Issue (4): 468-484.doi: 10.19788/j.issn.2096-6369.000130
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HA XiaoLin1(
), LI Jie2,*(
), YUAN YuHui1, ZHANG ZiYi1, LIANG MinYan2
Received:2025-08-22
Revised:2025-10-09
Online:2025-12-26
Published:2025-12-26
Contact:
LI Jie
HA XiaoLin, LI Jie, YUAN YuHui, ZHANG ZiYi, LIANG MinYan. Research on the Architecture and Key Technologies of Agricultural and Rural Data Fusion Service Platforms[J].Journal of Agricultural Big Data, 2025, 7(4): 468-484.
Table 1
Technology selection based on scenario requirements"
| 数据服务类型 | 技术选型 | 农业场景示例 |
|---|---|---|
| OLAP分析 | 列式存储:ClickHouse、Doris | 跨年气象数据与产量的关联分析 |
| 数据湖表:Delta Lake、Iceberg | 联合分析土壤数据、施肥记录与遥感影像元数据 | |
| 实时查询与缓存 | 内存数据库:Redis、KeyDB | 农产品实时价格看板(缓存热点数据) |
| 文档数据库:MongoDB | 存储动态格式的农场管理记录(含嵌套JSON) | |
| 时空数据查询 | 时空数据库:PostGIS+TimescaleDB | 查询某区域过去一周的病虫害分布热力图 |
| 全文检索与图谱 | 搜索引擎:Elasticsearch | 搜索包含“水稻霉病防治”的科研文档 |
| 图数据库:Neo4j、NebulaGraph | 分析作物品种、病虫害、农药之间的关联网络 | |
| 时序数据存储 | 时序数据库:InfluxDB、TDengine | 存储传感器秒级温湿度数据,支持预测灌溉时机 |
| 低代码可视化 | OLAP Cube: Kylin、Druid | 为BI工具预计算“各地区作物产量月度统计”数据集 |
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