Cloud Manifest Configuration for Industrial Data Accessibility
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Solution Overview
Problem
Industrial automation systems generate vast amounts of data that are typically limited to local access, restricting the ability to leverage this data for broader analytics and reporting across geographically diverse industrial enterprises.
Innovation Solution
A cloud computing platform with an agent-based architecture that collects, processes, and stores industrial data from various sources, using priority queues and manifest assemblies to determine processing based on customer-specific requirements, enabling remote access and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If industrial data is stored and processed locally only, then data security and access control are maintained, but data accessibility and ability to perform enterprise-level analytics across multiple facilities are restricted
Solution Approach 1:
The system segments data management into local and cloud components. Local agents collect and pre-process data at individual facilities, while the cloud platform aggregates data from multiple sources. This segmentation enables remote access to enterprise-level analytics while maintaining simple local operations, resolving the contradiction between data accessibility and system complexity.
Solution Approach 2:
The patent introduces a cloud-based data lake as an intermediary layer between local industrial systems and enterprise analytics applications. This intermediary aggregates data from multiple facilities, provides unified access controls, and enables cross-facility analytics without requiring complex direct connections between all systems, thus improving accessibility while managing complexity.
2Productivity
If all industrial data is collected and processed in a centralized cloud platform, then enterprise-level analytics and reporting capabilities are enhanced, but data transmission requirements and network infrastructure complexity increase
Solution Approach 1:
Local agents perform preliminary data collection, filtering, and aggregation before transmitting to the cloud platform. This preliminary action reduces the volume of data requiring transmission and simplifies network infrastructure requirements while still enabling comprehensive enterprise-level analytics at the cloud level, resolving the contradiction between analytics capability and network complexity.
Solution Approach 2:
The system implements local quality by enabling each facility to pre-process and aggregate its own data locally before cloud transmission. This local processing reduces network bandwidth requirements and infrastructure complexity while the centralized cloud platform maintains high analytics processing capability through aggregated enterprise-wide data.
3Loss of information
If data is aggregated from multiple geographically diverse facilities, then enterprise-wide analytics value is increased, but data management complexity and storage requirements increase
Solution Approach 1:
The cloud platform implements a universal data lake architecture that can ingest and manage diverse data types from multiple facilities through a single unified interface. This multi-functional platform handles data aggregation, storage, processing, and analytics in one system, increasing enterprise data value utilization while managing data management complexity through standardization rather than requiring separate systems for each facility.
Data Source
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AI summary
Cloud-based data processing services facilitate collection and processing of industrial data in a cloud platform. On-premise data collection agents collect and pre-process industrial data from one or more data sources, including industrial devices, historians, etc. The agents apply a header to the data defining a hierarchical, customer-specific data model that can be leveraged in the cloud platform to suitably process the data. Cloud-side data process services receive the resulting data packets, assign the data to one or more priority queues, and invoke a manifest assembly corresponding to the data model defined by the header. The manifest assembly defines one or more operations to be performed on the received data, including specifying a final storage destination for the data, determining one or more metrics for an industrial system or process based on the received data, or other such operations.