This project is about building an advanced AI-powered system that leverages multi-camera vision and large language models (LLMs) to optimize factory production processes. The system will analyze video feeds from multiple cameras, drones, and other sensors to provide actionable insights, suggest improvements, and automate processes where possible. The goal is to create a digital twin of the factory that can continuously learn, adapt, and optimize operations, leading to increased efficiency, reduced costs, and improved productivity.
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Multi-Camera Vision Analysis: Analyze live video feeds from multiple cameras to understand factory processes, material flow, and employee activities.
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AI-Driven Suggestions: Provide suggestions for process improvements, automation opportunities, and optimal floor planning to streamline material flow from raw materials to finished products.
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3D Mapping and Digital Twin: Create a 3D live map or digital twin of the factory using photogrammetry or NVIDIA Omniverse, allowing for a comprehensive understanding of the factory layout and operations.
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Drone Integration: Use drones (flying, bipedal, or wheeled) to inspect hard-to-reach areas of the factory, with the LLM system autonomously operating the drones and integrating their data into the overall factory model.
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Contextual Understanding and Learning: Continuously save and update the factory's context as text or 3D files, allowing the system to learn and improve over time. The system will ask operators questions if it encounters something it doesn't understand.
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Document Retrieval and Integration: Automatically retrieve and understand documentation such as bills of materials, standard process documents, user manuals, ERP data, and CMS system information to enrich its understanding of the factory operations.
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Employee Monitoring and Dynamics Analysis: Monitor employee communications, dynamics, and roles to suggest optimal staffing levels, roles, and salary structures.
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Audio-Video Fusion: Utilize audio data from cameras to understand vibrations, machine sounds, and conversations, enhancing the system's contextual awareness.
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Periodic Inspections and Iterative Improvements: Periodically inspect the factory and iterate on AI recommendations, ensuring continuous improvement and optimization.
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Cost-Benefit Analysis: Provide estimated budgets and potential ROI for suggested improvements, helping managers make informed decisions.
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Communication with Operators: Interact with factory operators to clarify doubts, seek additional information, and explain suggestions in a human-friendly manner.
In today's fast-paced industrial world, factories need to be agile, efficient, and adaptive to stay competitive. This project aims to provide a comprehensive AI solution that can help factories achieve these goals by:
- Optimizing Production Processes: Identifying bottlenecks and suggesting improvements to streamline operations.
- Reducing Costs: Automating processes and optimizing resource allocation to reduce operational costs.
- Enhancing Safety: Monitoring factory conditions and employee activities to identify potential safety hazards.
- Improving Decision-Making: Providing data-driven insights and suggestions to support better decision-making.
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Data Collection:
- Collect live video feeds from multiple cameras installed in the factory.
- Use drones to inspect hard-to-reach areas and collect additional data.
- Retrieve and integrate documentation from ERP, CMS, and other systems.
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Data Processing:
- Use LLMs to analyze video feeds, audio data, and documentation to understand factory processes, material flow, and employee activities.
- Create a 3D digital twin of the factory using photogrammetry or NVIDIA Omniverse.
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Insights and Suggestions:
- Analyze the data to identify areas for improvement, suggest automation opportunities, and optimize floor planning.
- Provide cost-benefit analyses for suggested improvements.
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Continuous Learning:
- Continuously update the factory's context and learning by periodically inspecting the factory and iterating on AI recommendations.
- Interact with operators to clarify doubts and gather additional information.
This project is open-source and we welcome contributions from developers, AI researchers, and industry experts. Whether you want to add new features, improve existing ones, or help with documentation, your contribution is valuable!
- Star the Repository: Show your support by giving this repository a star ⭐.
- Contribute: Fork the repository and submit pull requests with your changes.
- Discuss: Join our discussion forum to share ideas, ask questions, and collaborate with other contributors.
- Step 1: Develop a system that can process feeds from 4 live cameras, generate scene, process, and time-based action contexts, and save them to a local file.
- Step 2: Enhance the system to handle dynamic camera feeds, allowing for the addition or removal of cameras as needed.
- Step 3: Integrate document retrieval and understanding capabilities, enabling the system to autonomously navigate and understand ERP and CMS systems.
- Step 4: Implement the 3D mapping and digital twin functionality, creating a comprehensive model of the factory.
- Step 5: Integrate drone operations and photogrammetry data into the system.
- Step 6: Develop the AI's ability to ask questions, interact with operators, and provide suggestions based on its understanding.
- Step 7: Implement cost-benefit analysis and ROI estimation for suggested improvements.
- Step 8: Continuously iterate and improve the system based on feedback and new data.
- README.md: This file provides an overview of the project, its goals, and how to get involved.
- Future Documentation: As the project progresses, we will add more detailed documentation, including technical specifications, API documentation, and user guides.
- Factory Managers: Looking to optimize production processes and reduce costs.
- Industrial Engineers: Interested in applying AI and machine learning to improve factory operations.
- AI Researchers: Exploring the intersection of computer vision, natural language processing, and industrial applications.
- Developers: Interested in contributing to an open-source project with real-world impact.
Join us in building a smarter, more efficient, and more adaptive factory of the future. Your contributions can help make this vision a reality!
Note: This README file is intended to be a high-level overview of the project. Implementation details, code, and technical documentation will be added in the future.
Star this repository ⭐ and stay tuned for updates! 🚀