LLM-based agent for natural language interpretation of IIoT time-series data in a bakery digital twin system

Abstract

Industrial Internet of Things (IIoT) deployments in food manufacturing generate substantial volumes of time-series sensor data, yet operational personnel frequently lack the technical expertise required to query and interpret this data directly. This paper presents an edge-oriented architecture that bridges the gap between raw IIoT telemetry and human-comprehensible insights by integrating a locally deployed Large Language Model (LLM) agent with a digital twin infrastructure for bakery production monitoring. The proposed system introduces two principal contributions: (1) a read-only REST proxy that mediates between the LLM agent and an InfluxDB time-series database, enforcing a constrained API contract that eliminates the risks of direct database access and arbitrary query generation; and (2) a tool-augmented LLM agent equipped with domain-specific prompting that transforms natural language questions from plant operators into structured API calls, retrieves sensor data, and returns contextualized interpretations via a Telegram messaging interface. The architecture was validated on a production bakery facility monitoring temperature, humidity, weight, and energy consumption parameters. Experimental evaluation across 50 representative operator queries demonstrated an interpretation accuracy of 92%, with an average end-to-end response latency of 4.7 seconds. The results confirm that the proposed approach enables non-technical personnel to access and understand complex IIoT data streams without specialized training, while maintaining strict data security through architectural isolation.