Jiangxi Lulin Paper
InIoT platform powered by KaiwuDB
Contact UsBusiness Background
As a typical process manufacturing sector, the paper industry widely adopts automated equipment, such as paper machines, rewinders, cutters and other facilities for standardized workflows, ensuring high productivity and stable product quality.
However, automation brings significant cost and environmental pressures to paper enterprises. Extensive management of equipment and human resources drives up operational costs, and fails to support real-time monitoring and precise control, resulting in resource waste.
Business Challenges
In complex industrial sites with multiple scenarios and equipment, traditional data architectures have become the main bottleneck restricting the digitalization of the paper industry.
Decentralized and Delayed Data Collection
There are a wide range of equipment (paper machines, sealers, PV power stations, etc.) distributed across the paper factory, with numerous energy data collection points, making it difficult to achieve unified data acquisition.
Lack of Data Integration and Analysis
Production data is scattered across separate tables that requires format conversion before integrated analysis. Data cannot be shared between systems.
Poor Visualization
Production and management data lacks comprehensive, clear visualization, making it difficult for managers to quickly grasp energy consumption trends and key insights.
High Security and Compliance Risks
Production data involves sensitive information such as process parameters and customer orders. The paper enterprise faces high risks in data leakage or loss without proper access control and backup mechanisms.
KaiwuDB Solutions
High-Performance Data Ingestion
Advanced Time-series Capabilities
Multi-Model Architecture
AI Predictive Analytics
Client Benefits
Reliable High-frequency Data Collection
The platform gateway enables round-the-clock collection of high-frequency time-series data (speed, temperature, sheet tension, etc.) from paper machines, dryer cylinders and other equipment. It supports multi-serial communication, one-to-many collection, multi-network backup and breakpoint resumption, ensuring complete, real-time and lossless data acquisition.
Improved Equipment Monitoring & Early Warning
Milli-second data ingestion ensures real-time collection of key parameters from critical equipment. Combined with anomaly detection algorithms, the system provides early fault warnings, reduces unplanned downtime and ensures continuous production.
Reduced Storage and Operational Costs
Compared with traditional databases, KaiwuDB uses columnar storage and high-compression algorithms to reduce space usage. Automatic downsampling and data expiration policies lower long-term storage costs and simplify database operations.