Asset Data Distribution Across Edge, Plant, and Cloud Storage
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Solution Overview
Problem
Traditional centralized and cloud-based storage systems for industrial asset data face challenges with scalability, high operational costs, latency, and inability to anticipate future data access needs, leading to inefficiencies and potential loss of valuable insights.
Innovation Solution
A distributed data storage system that distributes asset data among multiple storage locations, including local, edge, and cloud devices, based on usage patterns and predictive analytics, optimizing data management through edge computing and dynamic resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If cloud-based centralized storage systems are used to store high-frequency asset data, then storage capacity and scalability are improved, but operational costs and bandwidth requirements increase prohibitively
Solution Approach 1:
The patent segments the centralized cloud storage system into a distributed hierarchical storage architecture with multiple levels: edge storage devices at industrial plants, regional storage facilities, and cloud storage. This segmentation allows data to be stored closer to its source (reducing bandwidth usage) while maintaining the scalability of cloud storage for archival purposes. The segmentation resolves the contradiction by eliminating the need to store all high-frequency data in centralized cloud, thereby reducing operational costs and bandwidth requirements while preserving adequate storage capacity through the distributed network.
Solution Approach 2:
The patent implements local quality by enabling industrial plants to store and process their own asset data locally at edge storage devices. This local storage capability ensures that frequently accessed data remains nearby, reducing the need for continuous bandwidth consumption to access cloud storage. Meanwhile, less frequently accessed data can be archived in cloud storage. This local quality approach resolves the contradiction by optimizing the balance between local storage efficiency and cloud storage scalability.
2Quantity of substance
If data is stored in centralized cloud servers, then storage scalability is improved, but data access latency increases for real-time operations
Solution Approach 1:
The patent segments the centralized cloud storage into a distributed hierarchical architecture where data is stored at multiple levels: edge storage devices at industrial plants for immediate access, regional storage facilities for regional access, and cloud storage for archival. This segmentation resolves the latency issue by ensuring that frequently accessed real-time data resides locally at edge devices, eliminating the need to retrieve it from distant cloud servers, while maintaining the scalability of cloud storage for less time-sensitive data.
Solution Approach 2:
The patent introduces edge storage devices and regional storage facilities as intermediary layers between industrial plants and centralized cloud storage. These intermediaries cache frequently accessed data closer to the data consumers, thereby reducing access latency for real-time operations. At the same time, the intermediary layers maintain connections to cloud storage, preserving the scalability benefits. This intermediary approach effectively resolves the contradiction between storage scalability and data access latency.
3Loss of energy
If only selected datasets are stored in cloud systems, then storage costs are reduced, but data availability and flexibility decrease when future needs arise
Solution Approach 1:
The patent segments data storage into different hierarchical levels with different retention policies: hot data (frequently accessed) stored at edge devices, warm data (moderately accessed) stored at regional facilities, and cold data (rarely accessed) stored in cloud archival storage. This segmentation allows the system to optimize storage costs by not duplicating all data across all levels, while maintaining data availability and flexibility through the distributed architecture. When future needs arise, data can be retrieved from appropriate levels without requiring complete re-storage, thus resolving the contradiction between cost reduction and adaptability.
Solution Approach 2:
The patent implements preliminary action by pre-distributing data across the distributed hierarchical storage network before it is needed. Usage patterns are analyzed and predicted, and data is proactively placed at appropriate storage levels in advance. This preliminary distribution ensures that when future data access needs arise, the data is already available at the optimal location, maintaining flexibility and adaptability without requiring costly emergency data retrieval or re-storage operations.
4Speed
If local storage systems are used at industrial plants, then data accessibility and response time are improved, but storage capacity and analytical sophistication are limited
Solution Approach 1:
The patent segments the storage architecture into a hierarchical distributed system where local edge storage devices provide fast access to frequently used data, while regional and cloud storage facilities provide additional capacity for less frequently accessed data. This segmentation resolves the contradiction by allowing industrial plants to maintain local storage for immediate data accessibility and response time, while leveraging the expanded capacity of the broader distributed network for archival and advanced analytics without requiring all data to be stored locally.
Data Source
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AI summary
The disclosure relates to a method (100) for distributing asset data among multiple storage locations of a distributed data storage system (10) for one or more industrial plants (2), the asset data relating to one or more assets of the one or more industrial plants (2), the method comprising: - obtaining usage data indicative of a usage of the asset data; and - distributing the asset data among the multiple storage locations based on the obtained usage data, wherein the asset data is distributed among storage locations of different entities of the distributed data storage system (10), the entities including at least one storage device (20) of the one or more industrial plants (2), at least one edge device (30) and at least one storage device of a cloud server (40).