Collaboration Ambient Data Model

General Description

The Collaboration Ambient Data Model is a component that provides a data model that standardize the information stored and managed for ambient reasoning. It is based on industrial integration standard IEC-62264, defining different parts of the production process, indluding: - Production specifications such as operations, materials, personnel or requirements. - Capability - Production scheduling - Performance information.

It is designed to be flexible and scalable, providing a robust solution for storing and managing large amounts of data, and integrating different information types and attributes as needed. The data model is implemented through a relational database, designed to be easy to use and understand, as well as to provide a high level of performance and scalability, and to be easily integrated with other components.

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Contact

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Name email Organisation
Laura Moya Ruiz lmoya@cigip.upv.es logo image

License

MIT License

Technical Foundations

✔️ Describe the technical foundations of your component. Describe in detail the models, algorithms, or methods you use. If the documentation is available in a different source, for instance, an open access paper, provide here a high-level overview and links to external references.

The AI-PRISM Reasoning Model is a data-driven framework designed to model the structure and operational aspects of an industrial plant. This model provides a structured representation of manufacturing environments by defining key elements such as plant hierarchy, resource allocation, capability assessment, and production execution. Below is an overview of the key technical foundations underlying the model.

  1. Hierarchical structure of the industrial plant The model organizes the factory plant layout into a structured hierarchy that helps define areas, equipment, personnel and processes, which are the elements used to define the production flow and resource allocation. The hierarchical structure is essential for understanding the physical distribution. To do so, the model uses three different hierarchy models:
  2. Functional hierarchy. It defines the level of operations, from physical processes (level 0) to business processes (level 4).
  3. Role-based equipment hierarchy. It organizes assets based on their function and role in the production process.
  4. Physical asset equipment hierarchy. It groups assets based on their physical location and logical grouping within the plant.

This hierarchy provides a basis for mapping resources, production workflows, and information exchange across different levels of manufacturing operations.

  1. Data mapping and resource modeling The model defines the relationships between different manufacturing elements, such as products, resources, operations, and schedules, using a data mapping approach. It categorizes elements into:
  2. Resources. Human personnel, equipment, and materials.
  3. Operations. The manufacturing steps required to produce a product.

Resources are further divided into resource classes, which define common attributes for similar elements. These include equipment classes, personnel classes, and material classes, each with specific properties and test specifications. Through the definition of these elements, the model captures the essential aspects of the manufacturing process, enabling a comprehensive representation of the production environment and its capabilities. An example of each type of resource structure would be: | Person | Material |

Category Data
Equipment { "id": 1, "code": "Painting booth 1", "description": { "en": "Painting booth","es": "Cabina de pintura" },"parent_id": 1 ("Painting area"),"location": 2 ("Painting area"),"hierarchy_scope": 4 ("Work center"),"equipment_class": 1 ("Painting booth") }
Person { "id": 1, "code": "John", "description": { "en": "John","es": "Juan" },"parent_id": null }
Material { "id": 1, "code": "H-A Solvent", "description": { "en": "Hydro-alcoholic solvent","es": "Disolvente hidroalcohólico" },"parent_id": null ,"material_class": 1 ("Paint"),"assembly_type": "Physical","assembly_relationship": "Permanent" }
  1. Manufacturing process and operations The AI-PRISM model structures the manufacturing process through a layered approach that includes:
  2. Process segments. The smallest generic unit of an operation.
  3. Operation segments. The application of a process segment to produce a specific product, adding dependencies, duration, and resource requirements.
  4. Resource specifications. The link between an operation segment and the resources needed to execute it, defining the exact personnel, equipment, and materials needed for each operation segment.

Operations are linked to bills of materials (BOM) and bills of resources (BOR), ensuring traceability and consistency in the production process.

  1. Capability model The capability model is an extension of the production information model that focuses on the resources available to execute operations effectively.It is designed to assess and optimize the execution of the manufacturing process. The model includes:
  2. Operation capability. It evaluates the ability of an operation to meet production goals.
  3. Process segment capability. It assesses the performance of a process segment within a specific manufacturing step.
  4. Equipment and personnel capability. It analyzes the availability, confidence level, and utilization of machines and workers in the production process.

The model includes confidence factors to represent the reliability of capability assessments, providing a quantitative measure of the production environment's performance, along with defined calendars to track operational periods. By integrating these capabilities, the model enables a comprehensive evaluation of the manufacturing process.

  1. Scheduling model

Production scheduling is a key element in industrial operations. It ensures the efficiency in allocating resources and assigning tasks based on available resources and operational constraints, so that production goals are met. To do so, it supports: - Operation schedule. It is defined as a structured plan detailing the sequence and timing of production tasks. It accounts for dependencies between operations, ensuring a smooth workflow. - Operation request. It defines a specific operation to be performed within a scheduled time window, including its assigned resources, such as the operation type, duration, and resource requirements. - Segment requirements. It specifies the personnel, equipment, materials, and other necessary elements for each operation segment (each step of the process), ensuring that all resources are available when needed.

  1. Production Performance Once the production schedule is established, the AI-PRISM model tracks real-time execution data to monitor performance and ensure alignment with planned objectives.

  2. External references

Integrated and Open Source Components

This section outlines both pre-existing technologies and open-source components that have been integrated into the component. It provides details on their sources, purposes, modifications, and compliance with licensing terms.

Overview

The AI-PRISM Reasoning Model has been developed as a custom-built PostgreSQL database following the IEC-62264 standard for industrial integration. The database model has been created from scratch, along with an additional Node-RED application that has been developed to facilitate data loading. This application reads structured data from Excel files and fills the database at the time of installation.

Pre-existing Components

The AI-PRISM database model itself does not rely on external open-source components. However, the data-loading process leverages a Node-RED application.

Data loader

Source

The Node-RED application used for data loading is an open-source project available on GitLab here.

Description

The Data Loader is a custom-built application that automates the process of filling the AI-PRISM database with structured standard data. It extracts structured information from an Excel file placed in a specific folder and loads it into the database tables whenever there is a change in it. The application is designed to be transparent to the user, meaning it works in the background without requiring manual intervention, processing all new information or changes in the data source and applying them into the database following the data model.

Modifications

✔️ Describe the modifications made to the sub-component to adapt it to the AI-PRISM project. Describe enhancements, or additional features that have been generated thanks to AI-PRISM.

Purpose in AI-PRISM

The Data Loader is a useful component for automating the database filling process when the user of the data model works with data in Excel format. This way the user only needs to exchange the Excel file with the new one or upload its information in the folder, so the component can create or update data in the database through Excel files easily.

License

MIT License

How to install

✔️ Every AI-PRISM component is installed using the Cluster management service. During the installation process, the user needs to configure a set of high-level parameters. Provide here a description of the parameters that the user needs to fill in.

How to use

The AI-PRISM Collaboration Ambient Data Model operates as a relational database that supports ambient reasoning in industrial environments. Users can interact with the data model through: - Direct database queries using SQL. - Backend API that connects with other components. - Frontend application that retrieve and visualize data. - Automated data ingestion through the Node-RED data loader.

Data management

Depending on the user's role, the data model can be used through different interfaces. 1. SQL queries. Users with database management skills can interact with the data model directly through SQL queries. This allows for custom data extraction, transformation, and loading processes, as well as the creation of custom reports and dashboards. 2. Backend API. If the data model is integrated with a backend application, as it happens with the HRC Painting Scheduler, a backend API will allow that component to interact with the database. This API can be used to retrieve data, update records, and perform other operations programmatically. 3. Frontend application. When the data model is connected to a frontend application, it will provide an intuitive interface for interacting with the data model, so that users are able to view information such as schedule information. 4. Node-RED data loader. The Node-RED application is used to automate the process of filling the database with structured data. A folder is created so that users can add and work on a Excel file that would be red by the Node-RED application and fill the database with the information or changes in that file.