Tiered Historian Replication via Summary Tags
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Solution Overview
Problem
Large industrial enterprises face challenges in managing and replicating vast amounts of time-series data from multiple geographically dispersed locations to a centralized historian, leading to data volume and network communication overload issues.
Innovation Solution
Implementing a tiered historian system that supports disconnected replication, re-synchronization of out-of-order data, and summary replication tags to efficiently manage and transmit data from lower-tier historians to a centralized upper-tier historian, including features like store-forward memory, synchronization queues, and autonomous data generation and configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all time-series data from multiple lower-tier historians is replicated to the centralized upper-tier historian, then data completeness and centralization are improved, but network traffic and processing load increase significantly
Solution Approach 1:
The patent extracts only the essential or changed data from the lower-tier historians for replication to the upper-tier historian, rather than transmitting all time-series data. This selective extraction reduces network traffic while maintaining data completeness at the centralized level.
Solution Approach 2:
The data replication process is segmented into different tiers with selective replication rules. Lower-tier historians replicate data to intermediate tiers, and only specific data sets are propagated to the upper-tier historian, dividing the overall data flow into manageable segments that reduce total network load.
2Speed
If data is replicated in real-time from lower-tier to upper-tier historians, then data freshness is improved, but network communication overhead increases
Solution Approach 1:
Instead of continuous real-time replication, the system uses periodic replication intervals where data is transmitted at scheduled times. This periodic action maintains data freshness while significantly reducing network communication overhead compared to continuous real-time transmission.
Solution Approach 2:
Data is prepared and staged in local buffers at lower-tier historians before replication, allowing batch transmission during periodic intervals. This preliminary preparation enables efficient bulk transfers that maintain data freshness without requiring constant network communication.
3Reliability
If the centralized historian processes all incoming data from multiple locations, then data centralization is improved, but processing load and system complexity increase
Solution Approach 1:
The processing workload is segmented across multiple tiers. Lower-tier historians perform initial data processing and filtering, intermediate tiers handle regional aggregation, and the upper-tier historian focuses on enterprise-wide consolidation. This segmentation distributes processing load while achieving data centralization at the appropriate hierarchical levels.
Solution Approach 2:
Intermediate-tier historians act as mediators between lower-tier data sources and the upper-tier centralized historian. These intermediaries perform data aggregation, filtering, and preprocessing, reducing the processing load on the upper-tier system while maintaining data centralization capabilities.
4Stability of the object's composition
If the system maintains strict synchronization between all historians, then data consistency is improved, but system robustness and autonomy decrease
Solution Approach 1:
The synchronization mechanism is made dynamic, allowing historians to operate in different synchronization modes depending on their connection status. When connected, strict synchronization maintains data consistency; when disconnected, autonomous operation maintains system robustness. The system dynamically adjusts synchronization strictness based on operational conditions.
Solution Approach 2:
Synchronization parameters such as consistency requirements and replication frequency are changed based on system state. During normal operation, strict consistency parameters are applied; during disconnection or failure conditions, parameters are relaxed to allow autonomous operation, thereby maintaining both data consistency and system robustness under different conditions.
Data Source
AI summary
A multi-tiered replicated process database and corresponding method are disclosed for supporting replication between tiers. The multi-tiered replicated process database comprises a tier one (T1) database server computer including a process history database and a replication service. The replication service includes a set of accumulators. Each accumulator is adapted to render a summary T2 database tag data structure from a set of data values retrieved from the process history database for a specified T1 database tag. The replicated database system also includes a tier two (T2) database server computer comprising a consolidated database that includes process data replicated from a set of T1 database servers. At least a portion of the process data replicated from the set of T1 database servers is summary T2 database tag data rendered by the set of accumulators.