Virtualized Factory Data Platform for Secure AI-Driven Control
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Solution Overview
Problem
Current manufacturing environments face challenges in data collection and facility control due to limited access to data sources, incompatibility of hardware and software systems, security concerns, and the inability to integrate advanced analytics and AI into data management systems, leading to incomplete decision-making and inefficiencies across global operations.
Innovation Solution
A hybrid data analytics and manufacturing facility control system that allows secure data gathering, storage, and analysis, accessible both on-site and in the cloud, with AI and machine learning capabilities for anomaly detection and process optimization, enabling seamless integration of third-party apps and virtual reality, and providing redundancy for critical applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data collection and facility control operations are moved to a centralized cloud location, then data accessibility and analytics capability are improved, but security risks and vulnerability to hacking increase
Solution Approach 1:
The system segments data and control operations into multiple virtual machines (VMs) with different access levels. Sensitive data is isolated in secure VMs while analytics operations run in separate VMs, reducing the attack surface and limiting potential damage from security breaches.
Solution Approach 2:
The patent introduces an intermediary layer of virtualization between physical hardware and data access points. This virtual machine layer acts as a buffer that provides secure access controls, authentication mechanisms, and isolation, allowing cloud-based accessibility while mitigating direct security risks to underlying infrastructure.
2Adaptability or versatility
If multiple factories use different hardware and software systems, then local operational flexibility is maintained, but data integration and analysis capability deteriorate
Solution Approach 1:
The virtual machine architecture provides a universal platform that can interface with multiple different hardware and software systems. The VM layer abstracts underlying system differences, enabling data collection and analytics across diverse factory environments through a common interface without requiring standardization of local systems.
Solution Approach 2:
The patent uses virtual machines as an intermediary layer between diverse factory systems and the central analytics platform. This intermediary handles protocol translation, data normalization, and interface standardization, allowing local operational flexibility to be maintained while simplifying data integration at the central level.
3Loss of information
If advanced analytics and AI capabilities are integrated into the data management system, then decision-making quality is improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments analytics workloads into separate virtual machines dedicated to different AI and advanced analytics functions. This segmentation allows complex computational tasks to be isolated and managed independently, reducing overall system complexity while enabling high-quality analytics through specialized processing units.
Solution Approach 2:
The patent adds a virtualization dimension to the system architecture, creating a layered structure where complex AI analytics operations can be executed in isolated virtual environments. This dimensional addition allows sophisticated decision-making capabilities without directly increasing the complexity of the underlying physical infrastructure.
4Adaptability or versatility
If third-party applications are added to extend system functionality, then operational capability is improved, but integration difficulty and communication challenges increase
Solution Approach 1:
The virtual machine platform provides a universal execution environment that can host multiple third-party applications with different functionalities. This universal base simplifies integration by providing consistent interfaces, shared resources, and standardized communication protocols, reducing the complexity of integrating diverse third-party solutions.
Data Source
AI summary
The disclosure is directed to a system for integrating and centralizing multiple manufacturing software types into a consolidated platform. The system interfaces with third party software and performs data collection, data analytics, factory controls, virtual modeling, and checklist creation, as well as many other manufacturing applications. Artificial intelligence and machine learning are also integrated into the platform to assist with root cause analysis and increasing production efficiency.