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Wenzhou Deyuan Electric Integrates ZigBee Tags for Warehouse Management

在大型仓库中寻找货物费时费力,

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December 17, 2025
Warehouse Item Retrieval

Locating items in a large warehouse is time-consuming and labor-intensive. Even when materials are labeled on shelves, it remains a cumbersome task for users. They must carefully identify the goods on each shelf, which requires significant time and considerable patience.

Wenzhou Deyuan Electric, by integrating its Warehouse Management System (WMS) with West-HN's ZigBee tags, has implemented an intelligent item retrieval solution. This system enables on-demand searching, saving time, reducing errors, and significantly improving efficiency.

Application Diagram

  • Bind goods or containers with tags.
  • Bluetooth card readers receive tag information.
  • Reader data is integrated with the customer's warehouse system.
  • Personnel use handheld terminals to locate items. Commands are sent via the handheld device to call specific tags.
  • Customers configure shelf lights on each rack. During retrieval, in addition to the tag flashing, the shelf lights also illuminate to facilitate rapid area identification.

About Wenzhou Deyuan Electric

Wenzhou Deyuan Electric Co., Ltd. (now integrated as Longsen Electric) was founded in 1994. It is a one-stop supplier for complete electrical equipment cabinets, from components to finished products, primarily engaged in the R&D and production of high and low voltage switchgear-related products. Its clients span over 100 countries, serving the needs of more than 500 users worldwide.

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Frequently Asked Questions

Our solution provides comprehensive digital infrastructure that integrates environmental sensing, automated control, spatial positioning, and AI-driven analytics, enabling unprecedented operational efficiency and yield optimization.
Yes. Our IoT controllers support standard industrial protocols (RS485, Modbus) and various I/O expansion modules, allowing seamless integration with most existing agricultural equipment.
The AI engine analyzes historical environment data against yield results to suggest optimal setpoints, predicts equipment failure before it happens, and identifies operational bottlenecks through data analytics.
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