Modular Manifest Generation for Cloud Industrial Data Configuration
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Solution Overview
Problem
Current industrial data collection systems are limited by the need for on-site applications and devices to share a common network with industrial controllers, restricting access to data and requiring labor-intensive configuration of cloud-based data collection, especially in geographically diverse industrial enterprises.
Innovation Solution
A manifest generation system that imports industrial controller program files, extracts data tag information, and generates system and data manifest files to configure cloud-based industrial data collection, allowing for the selection and organization of data tags for collection, thereby simplifying the configuration process and enabling remote data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based data collection is implemented for industrial data, then data accessibility and remote processing capability are improved, but configuration complexity and time consumption increase due to the large number of data points from multiple automation systems
Solution Approach 1:
The patent segments the configuration process into two distinct manifest files: a system manifest that defines the hierarchical data model structure, and data manifest files that contain specific data point definitions. This segmentation allows the complex configuration to be divided into manageable parts, where the system manifest establishes the framework once and can be reused, while individual data manifest files can be independently configured and combined.
Solution Approach 2:
The patent implements preliminary action by establishing the system manifest with hierarchical data model definitions before configuring specific data points. The system manifest pre-defines the structure, relationships, and metadata schemas that will govern data collection, allowing subsequent data manifest files to simply reference these pre-established frameworks rather than redefining them for each data point.
2Quantity of substance
If manual configuration of cloud-based data collection is performed for numerous data points, then data collection coverage is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent enables copying and reuse of the system manifest configuration across multiple data manifest files and different automation systems. The hierarchical data model structure defined in the system manifest can be copied and referenced repeatedly, allowing rapid configuration of new data collection scenarios without manually recreating the entire data model framework for each system or data set.
3Reliability
If on-site applications are used to access industrial data, then network security and data access reliability are maintained, but geographical flexibility and remote access capability are limited
Solution Approach 1:
The patent introduces manifest files as intermediary configuration artifacts that enable secure cloud-based data collection. These manifests act as formal contracts between the automation systems and cloud platform, defining data models, relationships, and collection parameters in a standardized format. This intermediary layer ensures reliable data access while enabling geographical flexibility, as the standardized manifests can be deployed to cloud platforms that provide secure remote access capabilities.
Data Source
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AI summary
A manifest generation system generates a system model for a cloud computing architecture. The system generates the system model in the form of system, data, and metrics manifests that act as an information concentrator for configuring various aspects of data ingestion and data management. The manifest generation system leverages both information extracted from industrial devices, applications, and programs that make up physical industrial automation systems, as well as user selections identifying which data tags are to be collected, specifying data collection preferences, etc. In this way, manifest data for configuring cloud-level data monitoring and collection is mapped to the automation and control system configurations via information extracted from the system-level topology. This approach can automate and simplify aspects of the cloud-based data collection configuration process.