By establishing a smart logistics data warehouse, building a logistics cloud computing platform, and real-time processing of logistics information, complete the storage, calculation and real-time stream processing of smart logistics information, providing a complete data preparation for smart logistics.

(1) Smart Logistics Data Warehouse

The data warehouse technology is mainly to collect and process data in an integrated manner, and continuously organize the data in the information system to provide decision support for decision makers. Data warehouse technology mainly solves problems such as data extraction, integration and data performance optimization. The data warehouse technology in the context of big data mainly includes Hive, Hadoop DB, Hadapt and so on.

(2) Smart Logistics Cloud Computing Platform

In the cloud computing smart logistics mode, users can save data in the data center of the Internet, and the application runs on the Internet large-scale server cluster, as shown in Figure 1-2. The cloud computing service provider is responsible for data management and maintenance to ensure the normal operation of the data. The intelligent logistics management platform provides users with sufficient storage space and sufficient computing power to uniformly deploy the entire social logistics data resources. Terminals such as the Internet and computers use data and services quickly and easily, and enjoy high-performance computing and application services. The intelligent logistics management platform under cloud computing can reduce the investment and use cost of enterprises in the logistics platform, and lower the threshold for enterprises to realize the informationization of intelligent logistics management, thereby enhancing the competitiveness of enterprises.

(3) Real-time processing of intelligent logistics information

In the era of intelligent logistics information, real-time stream processing requires real-time and continuous use, and uses Hadoop platform, Flume, Kafka and other open source technologies to complete real-time data storage, real-time computing, real-time analysis, etc. Mining data values, completing value payments, and providing data connectivity to other online production systems (data feedback).

Reprinted from the network