Customer Cases / Industrial IoT

Guangxi Jianhui Paper

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Enhanced equipment monitoring & warning efficiency
Enhanced equipment monitoring & warning efficiency

Business Background

As a typical process manufacturing sector, the paper industry widely adopts automated production lines to improve production efficiency and quality stability.

However, extensive management of equipment and labor drives up costs, and fails to support real-time monitoring and precise control. The resource waste has become core bottleneck hindering enterprises’ digital and green transformation.

Data Challenges

In complex industrial sites with multiple scenarios and devices, traditional data architectures have become a major bottleneck restricting the digitalization of the paper industry

01

Difficulties in Multi-Source Data Collection

Jianhui Paper employs a wide range of equipment across its factories, including paper machines and rewinders, which are distributed across multiple plants and workshops, with a large number of data collection points. Traditional collection methods suffer from delays, data loss or duplications, failing to provide timely data support for production adjustments.

02

Severe Data Silos

Business systems and IT systems operate independently, forming prominent data silos. Inconsistent data standards and difficult historical data access prevent a full view of production, making it hard to identify high-energy and low-efficiency processes.

03

Insufficient Data Visualization

Production and management data lack an intuitive, unified visualization interface. Managers cannot quickly grasp energy consumption trends and key production indicators. Data application is limited to post-event statistics, only supporting basic tasks such as monthly output summarization.

04

High Risks in Security and Compliance

Production data contains sensitive information such as core process parameters and customer orders. Inadequate permission control and data backup mechanisms may easily lead to data leakage or loss.

KaiwuDB Solution

High-Performance Time-Series Data Processing

KaiwuDB supports standard SQL ingestion and batch import, and it can achieve millions of rows of data ingested per second with nanosecond-level data precision.For massive time-series data read & write demands, it innovatively provides a time-series table mechanism. By setting primary key tags for different devices, data is automatically partitioned and indexed by tags during ingestion, enabling fast data positioning, high-performance query, and large-scale data aggregation, greatly improving database efficiency.

Multi-Model Data Integrated Management

KaiwuDB integrates general data models at the kernel layer, enabling unified storage and management of time-series and relational data. It supports unified access and processing of multi-model data, providing technical transparency for upper-layer applications. This feature perfectly matches the complex data needs of the paper industry.

Built-in AI Predictive Analytics

KaiwuDB is equipped with a pluggable AI prediction engine, supporting lifecycle management including model import, training, prediction, evaluation and updating. Developers without expertise in complex machine learning algorithms can deploy complete models with simple SQL functions. This enables equipment fault prediction, raw material ratio optimization, and provides strong support for predictive maintenance and precise cost control.

Client Benefits

Stable Collection of High-Frequency Data

Intelligent gateways enable round-the-clock collection of speed, temperature, sheet tension and other data from key equipment such as paper machines and dryer cylinders. The gateways support multi-serial communication, one-to-many collection, multi-network redundancy and breakpoint resumption, ensuring complete and lossless data acquisition and providing accurate, real-time data for production analysis.

Improved Equipment Monitoring & Warning Efficiency

KaiwuDB’s millisecond-level ingestion performance enables the system to collect real-time temperature, pressure, speed and other core parameters. With built-in anomaly detection algorithms, the system provides early warnings of potential equipment failures, effectively reducing unplanned downtime and ensuring stable, continuous production.

Lower Data Storage and Operations Costs

Compared with traditional relational databases, KaiwuDB uses columnar storage and high-efficiency compression to greatly reduce storage space. It also supports automatic data downsampling and expiration policies to automatically delete invalid historical data, cutting long-term storage costs, simplifying database operation workflows, and reducing management complexity.

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