Our Digital Competence

Possessing a stellar level of digital expertise, with deep knowledge and experience in all things digital.

Quality control management of raw materials

Digital blend design & management

Digital Twin Applications of Intelligent Control

Tobacco quality monitoring


Quality control management of raw materials

After a series of automated processes, such as raw material testing, data collection and organization, algorithm analysis, data dimensionality reduction, and model building, a complete raw material management system is established.

Establishment of a digitized quality database for the entire chain from base to warehouse.

Establishment of harmonized management of tobacco ecology, varieties, area, quality and environmental factors.

Quality analysis, characteristic analysis, variety adaptability analysis, layout analysis of raw materials, etc.

Matching of raw materials to product applications, demand planning, quality requirements, structural requirements, etc.


Digital blend design & management

Blend is the core of tobacco product making and value creation, through the assistance of molecular spectroscopy rapid detection and analysis platform, digitization of raw materials, design digitization, and product digitization, to realize the model innovation from empirical formula to scientific formula.



R&D Management Module

Product Analysis

Review Management

Progress Tracking


Market Testing



Digital Twin Applications of Intelligent Control

We have realized intelligent operation and maintenance technology based on digital twins, combining digital twin technology with artificial intelligence, big data analysis and other technologies to achieve intelligent management and optimization of the entire process of cigarette production.

Real-time monitoring

Utilizing sensors and IoT technology, real-time collection of data from operating equipment for real-time monitoring and real-time comparative analysis.

Intelligent Early Warning

By analyzing and modeling the monitoring data and using machine learning algorithms to analyze the equipment operation data, it realizes the prediction and early warning of equipment failures and automatically adjusts the operation parameters of the equipment.

Fault diagnosis

Simulate and analyze equipment and systems through digital twin models to help O&M personnel identify and troubleshoot problems.

Operation and maintenance optimization

By optimizing and adjusting the digital twin model, it can realize the self-maintenance and optimization of the equipment, reduce manual intervention, and lower the operation and maintenance cost.


Tobacco quality monitoring​

Relying on spectral characterization technology, it carries out tobacco similarity metrics, blend assistance design, and grade quality evaluation.

Blend maintenance

Raw material replacement

Blend optimization

Quality stability judgment & maintenance

Product management