GPLG · INDUSTRIAL SOFTWARE & DATA
Python Development
Python software for industrial data, automation, machine vision, AI and engineering workflows.
GPLG develops Python solutions that connect industrial information with software engineering, data processing and intelligent applications. From engineering utilities and production-data processing to machine vision and AI integration, the objective is software built around a real industrial requirement.
PYTHON IN INDUSTRIAL ENGINEERING
Turning industrial data into useful software functions
Industrial systems generate information from machines, sensors, PLCs, databases and production applications. Python can provide a flexible software layer for collecting, processing, analysing and using that information.
GPLG applies Python where its ecosystem and development flexibility provide a practical engineering advantage, while keeping real-time machine control in the systems designed for that responsibility.
Industrial control remains deterministic. Python extends what can be done with the information around that control.
INDUSTRIAL APPLICATIONS
Where Python can support the production environment
Data processing
Clean, transform, aggregate and evaluate production and engineering information.
Engineering tools
Automate calculations, file processing, configuration and repetitive technical workflows.
Machine vision
Build image-processing and inspection workflows around cameras and production requirements.
AI integration
Connect suitable machine-learning models and analytical functions to industrial data workflows.
PYTHON + INDUSTRIAL AUTOMATION
Software close to the production process
Python can connect engineering logic, industrial data and higher-level applications while PLCs and machine controllers continue to perform their dedicated control functions.
DATA & SOFTWARE ARCHITECTURE
From industrial source to engineering result
The architecture depends on what the application must accomplish. A clear separation between data acquisition, processing, application logic and output keeps responsibilities understandable and maintainable.
PYTHON WORKFLOW
PYTHON ECOSYSTEM
Tools selected according to the engineering problem
NumPy
Numerical operations and efficient handling of engineering data.
Pandas
Structured data processing, transformation and analysis workflows.
OpenCV
Image processing and machine-vision functions for suitable inspection applications.
PyTorch / TensorFlow
Machine-learning frameworks when AI forms part of the validated project architecture.
PYTHON WITHIN THE GPLG SOFTWARE ENVIRONMENT
Development, data, vision, AI and integration
A broad ecosystem allows Python applications to bridge multiple software disciplines within one engineered solution.
MACHINE VISION
Image processing connected to industrial workflows
Python can support machine-vision applications for image acquisition, processing, measurement, classification and inspection. The software architecture can then connect results to production data or higher-level systems.
Lighting, optics, camera selection, triggering and the physical inspection conditions remain as important as the software itself.
DATA ANALYTICS & AI
Use production information for more than storage
Where sufficient and relevant data exists, Python can be used to explore patterns, build analytical functions and integrate appropriate AI or machine-learning models.
The engineering objective comes first: the technology is selected because it solves a defined problem, not simply because AI is available.
ENGINEERING AUTOMATION
Automating repetitive technical workflows
Calculations
Formalise repetitive engineering calculations and validation rules.
Data preparation
Transform files and datasets into the format required by another engineering or production system.
Reporting
Generate structured outputs from production, quality or engineering information.
Integration utilities
Build dedicated tools that move or transform information between otherwise separate systems.
INDUSTRIAL CONNECTIVITY
Python as part of a connected architecture
Python applications can exchange information through APIs, databases, OPC UA, MQTT and other interfaces selected according to the equipment and project requirements.
Connectivity is designed with clear ownership of data and control responsibilities so that a higher-level software application does not unintentionally replace the role of the PLC or machine controller.
FROM REQUIREMENT TO DEPLOYMENT
A structured Python development process
Requirement analysis
Define the engineering problem, users, data sources and expected output.
Architecture
Define interfaces, processing stages, responsibilities and deployment environment.
Development
Implement clear modules around the application's actual engineering functions.
Integration
Connect the application to approved industrial and information systems.
Testing & validation
Validate processing, interfaces, expected outputs and relevant failure conditions.
Support & evolution
Maintain and extend the software as equipment, data and operational requirements change.
CONNECTED ENGINEERING
Python within the wider GPLG technology environment
Machine Vision
Industrial image acquisition, processing and inspection technologies.
Explore →PLG AI
AI capabilities integrated around defined industrial requirements.
Explore →Industrial IoT
Connected industrial data from equipment to higher-level systems.
Explore →OPC UA & MQTT
Communication technologies for industrial data exchange.
Explore →PYTHON DEVELOPMENT
Need a Python application built around your industrial process?
GPLG develops industrial Python software for engineering workflows, data processing, vision, analytics and system integration.