T/SAIAS 033-2025 Guidelines for the construction of industrial chain-of-thought datasets

Scope

This document specifies the term definitions, data resource classification, collection process, data production and quality evaluation, and data security requirements for the construction of industrial thinking chain datasets. This document is only applicable to the construction of datasets in three application fields of fault diagnosis, process optimization, and quality control in industrial scenarios. This document is suitable for industrial enterprises, industrial software developers, scientific research institutes, and other entities, and is used for the collection, production, and evaluation of thinking chain data for training complex reasoning models in industrial scenarios. This guideline systematically standardizes the construction process of industrial thinking chain datasets for the first time, covering data collection, production, quality evaluation, and security requirements. It focuses on three core scenarios in the industrial field: fault diagnosis, process optimization, and quality control. It proposes logical representation norms and data quality evaluation systems, aiming to provide a high-quality data foundation for training industrial AI models with complex reasoning capabilities and solve the data bottleneck of AI model reasoning training in complex industrial environments.

Development Information

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