Infrastructure Analysis and Requirements

The process begins with a full audit of facilities and transport systems: engineering networks, energy consumption, access control, video surveillance, and robotic or commercial vehicles. Key metrics are recorded: system load, operational efficiency, security, and incident frequency. A map of vulnerabilities and bottlenecks is created.

Control System Architecture

Multi-layered control systems are designed to integrate BMS, security systems, and intelligent transport management modules. Roles, access rights, integration points with external systems, and automated response scenarios are defined. The architecture emphasizes scalability, fault tolerance, and cross-platform compatibility.

Implementation
of ML-Driven Models

Machine learning models are applied to commercial transport and robotic systems for route prediction, speed and load optimization, and accident prevention. Models adapt to changing conditions and are trained on historical and live data to deliver accurate real-time decisions.

Security Systems and Monitoring

The solution includes intelligent security systems: video analytics, anomaly detection, automatic alerts, and access control. Automated response scenarios prevent accidents and minimize human error. Integration with BMS provides comprehensive control over facility and transport infrastructure.

Validation, Testing, and Integration

All components undergo simulation, modeling, and testing in real conditions and via digital twins. Integration with existing SCADA, PLC, and IoT systems ensures smooth deployment without downtime.

Measurable Impact

• Reduced accidents and operational risks by 30–40%.
• Optimized energy and equipment usage by 15–25%.
• Improved efficiency of commercial transport and logistics by 20–30%.
• Scalable system architecture for future expansion across new sites.
• Platform ready for automation, predictive maintenance, and Industry 4.0 implementation.