In the era of artificial intelligence, high-quality data is receiving policy emphasis. The 2026 Government Work Report proposed building a new form of a smart economy, specifically highlighting the need to deepen the development and utilization of data resources, improve the fundamental system for data as a factor of production, and construct high-quality datasets. The successful implementation of AI large models in the industrial sector relies on the support of high-quality industrial datasets.

What are High-Quality Industrial Datasets?

High-quality industrial datasets are the critical foundation for training, inference, and validation of artificial intelligence large models. They are collections of data processed through a series of intelligent steps—including collection, cleaning, categorization, and labeling—according to specific standards, and maintained with complete update and maintenance mechanisms.

High-quality industrial datasets cover all elements and the entire process of production, forming a complete data chain. They are updated in real-time and are precise and accurate; the accuracy of data collection equipment, the standards for data cleaning, and the procedures for data validation directly impact data quality. Standardization and normalization are the core keys and prerequisites for maximizing data value. They adhere to the bottom line of "security and compliance," ensuring data security throughout the entire lifecycle of data collection, storage, usage, and sharing. They achieve value transformation by uncovering underlying patterns and needs through technologies like big data analysis and AI algorithms, turning data from an "idle resource" into a "core asset."

How to Build High-Quality Industrial Datasets?

As a "pioneer" in the field of industrial AI, Luculent Smart leverages its technological and scenario advantages. By advancing simultaneously on three fronts—"technological platform innovation, vertical scenario cultivation, and ecosystem collaborative co-creation"—it has established a comprehensive pathway for building high-quality industrial datasets, driving the transformation of industrial data from a resource-oriented state to an asset-based, intelligent one.

(1) Strengthen the Technological Foundation: Achieve Full-Process Management of Datasets

Relying on the national-level "dual-cross" Luculent Suchang Industrial Internet Platform, a "one-stop" dataset management system has been built, integrating the entire process from data extraction, classification management, and correlation analysis to sample labeling and marketplace transactions. This system supports visual interface configuration for dataset extraction, multi-dimensional data filtering, and multi-modal data labeling (e.g., images, Q&A), effectively addressing pain points in various industrial sectors such as data multi-modality, format incompatibility, massive volume, and management difficulties. It provides "precise feeding" for large models dedicated to specific scenarios.

Furthermore, it enhances the core capabilities of the Suchang Yaoguang large model, including enterprise cognition, industrial knowledge understanding, and professional task orchestration. Simultaneously, as a continuously supplied "fuel," it feeds back and optimizes the underlying AI models, forming a virtuous cycle of "data feeding - model optimization - performance improvement," making the entire system "smarter" with use.

(2) Focus on Scenario-Driven Application: Unleash the Core Value of Data as a Factor

The Luculent Suchang "Industrial Brain" adheres to a scenario-oriented approach, focusing on core industrial segments such as R&D design, production manufacturing, operation and maintenance services, and operational management. It has created a series of high-quality dedicated datasets, including those for:

• Rotating Equipment Fault Diagnosis

• General Equipment Fault Warning

• Energy Consumption Optimization Models

• Large Models for Thermal Power Unit Safety Monitoring and Fault Warning Scenarios

• Large Models for Coal Mining Industry Equipment Operation and Maintenance Scenarios

• Intelligent Monitoring Systems for Thermal (Hydro) Power Units

These datasets are deeply integrated into actual industrial scenarios, optimizing production processes, enabling accurate fault prediction, driving intelligent operation and maintenance, and promoting energy conservation and emission reduction. They provide scientific support for enterprise intelligent decision-making, assisting industrial enterprises in their digital-intelligent transformation and upgrade, and ensuring that data value is truly realized and delivers tangible effects.

(3) Deepen Ecosystem Collaboration: Co-create and Share High-Quality Datasets

Collaborative co-creation between platform enterprises and industrial enterprises is a key pathway to promoting data integration and sharing, and empowering cross-industry, cross-domain digital innovation. Currently, Luculent Smart has partnered with leading enterprise clients in multiple industries, including power, coal mining, and metallurgy, to jointly build high-quality datasets and explore industry benchmarks and demonstration models.

By forming specialized teams combining "platform technical experts + industrial scenario specialists," and working deeply on project frontlines, heterogeneous data scattered across multiple systems and devices undergoes format normalization and noise treatment. This process forms high-quality, standardized, industry-specific datasets. Industry-general intelligent applications are then developed, promoting the rapid implementation and transformation of technological achievements, realizing the principle of "data co-creation, value sharing."

In the next phase, the company will collaborate with partners across "industry, academia, research, and application" to promote breakthroughs in key technologies, deepen scenario applications, strengthen standard development, accelerate the formation of an industrial data standard map, focus on cultivating high-level data talents with industrial backgrounds, and ensure that the construction of high-quality datasets yields results and demonstrates effectiveness.

The true implementation of Industrial AI is inseparable from the critical foundation of high-quality industrial datasets. After more than two decades of sediment and accumulation, Luculent Smart has systematically constructed a closed-loop chain of "raw data - high-quality datasets - intelligent applications." Moving forward, it will focus on the core directions of industrial data assetization and model application scenarioization, solidifying the robust foundation for AI to empower new industrialization.