AI‑Powered Real‑Time Multilingual Support and Terminology Management for International Logistics

In the current international logistics system, nine languages are supported, each with thousands of terms, leading to high translation costs, inconsistent terminology, and slow iteration cycles.

To solve these challenges, the team proposes an AI‑driven solution that leverages large language models (e.g., GPT) for real‑time, accurate translations, constructs a unified international terminology library, and empowers users to edit terms directly within the front‑end interface.

The plan includes seven key objectives: real‑time multilingual support, translation quality improvement via model‑learned data, faster term updates through a “Term Butler” feature, enhanced user participation with feedback‑driven suggestions, cost reduction by minimizing reliance on external translation vendors, increased system intelligence through behavior analysis, and continuous learning and optimization.

Implementation steps detail the integration of MCube’s template caching, expression and event engines for view rendering, and the use of large‑model platforms (GPT, Yanxi) for both batch language‑pack generation and on‑demand term translation, along with rule‑based constraints to ensure locale‑appropriate output.

Architectural components cover cloud‑based language‑pack storage, automated version updates, fallback mechanisms for offline use, and seamless deployment pipelines that keep language resources up‑to‑date without manual intervention.

Projected benefits include a reduction of roughly two person‑days per month in translation effort, higher translation accuracy, seamless offline support, efficient version compatibility, and easier maintenance through centralized term management.

The roadmap concludes with ongoing construction of the international terminology library, calls for community contributions, and a vision of continuously improving multilingual capabilities across the platform.