Data

Diverse datasets enhance manufacturing efficiency and quality control.

An industrial factory setting with machinery and equipment. The foreground contains numerous red plastic containers arranged in rows. Overhead, metal beams support the roof with large windows allowing natural light to illuminate the space. Pipes and control panels are visible, indicating a manufacturing operation.
An industrial factory setting with machinery and equipment. The foreground contains numerous red plastic containers arranged in rows. Overhead, metal beams support the roof with large windows allowing natural light to illuminate the space. Pipes and control panels are visible, indicating a manufacturing operation.
A large industrial factory setting with rows of textile machinery and spools of yarn. The machines are organized in parallel lines, and there are various structures supporting the production process. Overhead lights illuminate the space, and the floor has a tiled pattern. The presence of vibrant colors like orange and blue adds contrast to the metallic and neutral tones of the machinery.
A large industrial factory setting with rows of textile machinery and spools of yarn. The machines are organized in parallel lines, and there are various structures supporting the production process. Overhead lights illuminate the space, and the floor has a tiled pattern. The presence of vibrant colors like orange and blue adds contrast to the metallic and neutral tones of the machinery.

Sensing technology: Data is the "digital mapping" of the physical world, and sensors convert equipment status, environmental parameters, etc. into computable digital signals;

Intelligent technology: Data is the "fuel" of AI algorithms, and data patterns are mined through machine learning and optimization algorithms to generate decision instructions;

Sustainable technology: Data is the "quantitative basis" of green manufacturing, and supports the formulation and optimization of environmental protection goals through data such as energy consumption, emissions, and resource utilization.

Data collection: Collect equipment energy consumption data through smart meters and thermal imagers, and combine them with production plan data from the MES system;

Data analysis: Use energy consumption models (such as equipment energy efficiency matrix) to analyze high-energy consumption links, such as identifying equipment with abnormal standby energy consumption through clustering algorithms;

Decision-making application: Generate energy optimization solutions based on data, such as dynamically adjusting equipment start-stop strategies based on electricity price data and production load (case: a certain automobile factory reduced single-shift energy consumption by 12% through data-driven energy scheduling).

Innovative Solutions for Manufacturing Excellence

We collect and analyze diverse manufacturing data to enhance efficiency, quality, and sustainability in production processes through advanced intelligent frameworks and modular architecture.

A large, industrial machine component is centrally positioned in a factory setting. The machinery appears to be part of a mechanical assembly line, mounted on a sturdy black stand with a metallic surface. The background includes structural beams and various pieces of industrial equipment, with a focus on mechanical and robust engineering. The floor is painted a rusty red and the walls have industrial elements such as piping and storage areas.
A large, industrial machine component is centrally positioned in a factory setting. The machinery appears to be part of a mechanical assembly line, mounted on a sturdy black stand with a metallic surface. The background includes structural beams and various pieces of industrial equipment, with a focus on mechanical and robust engineering. The floor is painted a rusty red and the walls have industrial elements such as piping and storage areas.

Data Collection

Collecting diverse datasets from manufacturing equipment and processes.

An industrial setting inside a factory with multiple large mechanical components arranged in a row. A complex network of beams, pipes, and metal structures is visible above, indicating a manufacturing or assembly environment. The lighting is dim and utilitarian, focusing on functionality rather than aesthetics.
An industrial setting inside a factory with multiple large mechanical components arranged in a row. A complex network of beams, pipes, and metal structures is visible above, indicating a manufacturing or assembly environment. The lighting is dim and utilitarian, focusing on functionality rather than aesthetics.
Data Pre-processing

In cyber-physical manufacturing systems, data preprocessing is the key link between raw data and value mining, improving data quality: solving common problems such as noise interference, missing values, outliers, and format heterogeneity in industrial data;

Adapting to downstream applications: performing data standardization, feature extraction, and other conversions based on the needs of sustainable analysis, intelligent algorithms, and sensor fusion;

Reducing computing load: reducing edge-to-cloud transmission and storage costs through operations such as data compression and dimensionality reduction.

Intelligent Framework

Data preprocessing is the "foundation" for the implementation of sustainable, intelligent and sensor technologies in CPS manufacturing. Customized preprocessing strategies are required for data characteristics in different technical fields: sustainable data focuses on the continuity of time series, intelligent data emphasizes feature significance, and sensor data focuses on signal fidelity. In the future, with the deep integration of edge computing and AI, preprocessing will develop in the direction of "adaptive, unsupervised, and low latency", clearing quality obstacles for industrial data value mining.

Insights

Leveraging data for smarter manufacturing solutions.

© 2025. All rights reserved.