We propose a feasibility study for real-time automated data standardization leveraging Large Language Models (LLMs) to enhance seamless positioning systems in IoT environments. By integrating and standardizing heterogeneous sensor data from smartphones, IoT devices, and dedicated systems such as Ultra-Wideband (UWB), our study ensures data compatibility and improves positioning accuracy using the Extended Kalman Filter (EKF). The core components include the Intelligent Data Standardization Module (IDSM), which employs a fine-tuned LLM to convert varied sensor data into a standardized format, and the Transformation Rule Generation Module (TRGM), which automates the creation of transformation rules and scripts for ongoing data standardization. Evaluated in real-time environments, our study demonstrates adaptability and scalability, enhancing operational efficiency and accuracy in seamless navigation. This study underscores the potential of advanced LLMs in overcoming sensor data integration complexities, paving the way for more scalable and precise IoT navigation solutions.

翻译：本研究提出一项可行性研究，旨在利用大型语言模型实现实时自动化数据标准化，以增强物联网环境中的无缝定位系统。通过整合并标准化来自智能手机、物联网设备及超宽带等专用系统的异构传感器数据，本研究利用扩展卡尔曼滤波器确保数据兼容性并提升定位精度。核心组件包括智能数据标准化模块（该模块采用微调的大型语言模型将多样化传感器数据转换为标准化格式）以及转换规则生成模块（该模块可自动化生成用于持续数据标准化的转换规则与脚本）。通过在实时环境中的评估，本研究展示了系统的适应性与可扩展性，有效提升了无缝导航的运营效率与精度。该研究凸显了先进大型语言模型在克服传感器数据集成复杂性方面的潜力，为更具可扩展性和精确性的物联网导航解决方案开辟了道路。