Hybrid Cloud Data Storage for Industrial Process Historians
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Solution Overview
Problem
Current process historian applications face challenges in efficiently managing and analyzing large volumes of data from industrial tools, particularly in determining which data to store locally and which to store in the cloud, while ensuring data reliability and accessibility across distributed systems.
Innovation Solution
A hybrid approach using cloud computing that integrates a computing cloud with local systems, allowing data to be stored and processed based on real-time and non-real-time needs, utilizing a Service Oriented Architecture for efficient data distribution and access, and a partition model to determine data storage locations, enabling secure and efficient data management across multiple clients and systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all process data is stored locally in distributed systems, then data accessibility and real-time processing are improved, but device complexity and storage costs increase
Solution Approach 1:
The patent segments the data storage system into two parts: local storage for real-time process data and cloud storage for historical data. This segmentation allows the system to maintain fast local access for operational needs while offloading archival storage to the cloud, thereby reducing local device complexity and costs while preserving data accessibility.
Solution Approach 2:
The patent introduces a data management system as an intermediary that automatically determines which data to store locally and which to transfer to the cloud. This intermediary layer manages the complexity of data distribution, handling the logic of data placement, retrieval, and synchronization between local and cloud storage without requiring complex configurations from end users.
2Loss of energy
If process data is archived in the cloud, then storage costs and energy consumption are reduced, but data access speed and real-time processing capability deteriorate
Solution Approach 1:
The patent applies local quality by keeping data with different access frequency requirements in different locations. Frequently accessed real-time process data is stored locally for immediate access, while less frequently accessed historical data is archived in the cloud. This differentiated storage strategy ensures that data access speed is optimized for operational needs while still achieving energy and cost savings through cloud archiving.
3Reliability
If data is distributed across multiple locations, then system robustness and reliability are improved, but data consistency and management complexity worsen
Solution Approach 1:
The patent implements a data management system with feedback mechanisms that continuously monitor data synchronization status between local and cloud storage. When data is updated in one location, the system detects the change and automatically propagates it to the appropriate locations, ensuring data consistency across the distributed system while maintaining the robustness benefits of data distribution.
Data Source
AI summary
A system includes a computing cloud having at least one data storage unit and at least one processing unit. The computing cloud is configured to receive information associated with at least one process collected by an industrial tool, archive the process-related information, analyze the process-related information, and instruct a client device on a type of data to be cached by the client device. §The industrial tool could include a sensor configured to collect data associated with industrial equipment. Also, the client device may be associated with a local environment, the sensor may be configured to capture sensor readings at a specified interval, and the local environment may be configured to use a subset of the sensor readings. The client device can be configured to provide all of the sensor readings to the computing cloud.