建设包含信息采集、数据管理、决策分析和可视化展示的蜂业大数据平台，助力解决信息化水平低、行业信息不对称、管理效率低、蜂产品质量缺乏保障、蜂群养殖作业繁重等问题。

应用物联网、3S（Remote Sensing、Geography Information Systems、Global Positioning Systems）、人工智能、嵌入式、无人机、移动互联网、防伪溯源等技术，搭建蜂业大数据信息采集渠道，依据“1+1+1+N”的建设思想，采用包含数据采集层、数据资源层、业务支撑层、应用系统层和用户层等5层架构的设计模式，建设智慧蜂业大数据平台。

建立了总数据量约40T、包含蜂场环境、蜂群养殖、全产业链蜂产品质量安全和公共信息等多个维度智慧蜂业大数据中心，打造了智慧蜂业大数据管理平台和可视化系统，经过北京市密云区和湖北省竹山县示范应用，在“用数据生产”、“用数据管理”、“用数据决策”、“用数据服务”等方面取得了一定效果。

本研究从总体架构搭建、平台设计、数据库建设、技术实现和系统应用等方面进行了深入分析，实现了蜂业全产业链信息采集、智能管控、辅助决策及公共应用服务，有助于提升蜂业智能化、标准化、信息化水平，实现蜂产业的提质、节本和增效，全套技术方案和设计思路可为其他行业大数据平台搭建提供参考。

In order to help solve problems such as low level of informatization, asymmetry of industry information, low management efficiency, lack of guarantee for the quality of bee products, and heavy operations of bee farming, the research was carried out on building a big data platform for beekeeping, including information collection, data management, decision analysis and data visualization.

By means of technologies of internet of Things, 3S(Remote Sensing、Geography Information Systems、Global Positioning Systems), artificial intelligence, embedded, unmanned aerial vehicles, mobile Internet and anti-counterfeiting traceability, big data information collection channels in the apiculture were built. According to a five-layer design model, including the data collection layer, data resource layer, business support layer, application system layer and user layer, we developed a big data platform of intelligent apiculture based on the construction idea of "1+1+1+N".

A big data center of intelligent apiculture with a total data volume of about 40TB, which has multiple dimensions, such as bee farm environment, bee colony breeding, the quality and safety of bee products in the whole industry chain, and public information, and a big data management platform and visualization system for the intelligent apiculture have been established. After demonstration applications in Miyun District of Beijing and Zhushan County of Hubei Province, some achievements have been made in data production, data management, data decision-making and data service.

This research conducted in-depth analysis from the aspects of overall architecture construction, platform design, database construction, technology implementation and system application, the platform realizes the information collection, intelligent management and control, auxiliary decision-making and public application services of the entire chain of the bee industry. It can help improve the level of intelligence, standardization and informatization of the bee industry and conducive to the quality improvement, cost saving and efficiency increase. This set of technical solutions and design ideas can provide references for constructing big data platforms in other industries.