Alias Verification Through Persistent Cross-Platform Connections
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Solution Overview
Problem
Traditional identification techniques for process automation components using connection strings and node IDs are not human-interpretable and do not scale easily, leading to inefficiencies and potential system failures due to stale alias configuration files.
Innovation Solution
Implementing periodic alias verification using existing persistent connections between cross-platform clients and servers, such as OPC UA clients and servers, to quickly detect changes and avoid reconnecting to a Global Discovery Server for updated aliases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional identification techniques using connection strings and node IDs are used, then system setup is straightforward, but the identification is not human-interpretable and does not scale easily
Solution Approach 1:
The patent introduces an intermediary alias verification mechanism that operates between the client and server using existing persistent connections. This intermediary layer enables human-readable alias identification without requiring a complete system redesign, allowing gradual adoption and minimal disruption to existing architectures.
Solution Approach 2:
The system performs self-verification of aliases through automated discrepancy detection between local alias databases and server-provided mappings. This self-service approach eliminates the need for manual verification processes and reduces operational overhead while improving human interpretability.
2Productivity
If cross-platform clients periodically verify aliases using existing persistent connections, then detection of alias changes is quicker and network traffic is reduced, but additional verification logic is required
Solution Approach 1:
The system performs preliminary alias verification by obtaining locally updated lists of mappings from DCNs through existing persistent connections before discrepancies arise. This proactive approach enables quick detection of alias changes without waiting for system failures or manual interventions, improving productivity while using already-established connection infrastructure.
Solution Approach 2:
The verification mechanism implements feedback loops where clients periodically check alias mappings against DCN records and report discrepancies. This feedback system automatically detects and communicates alias changes, enabling rapid response to system modifications while maintaining relatively simple verification logic through standardized protocols.
3Loss of energy
If alias verification is performed through DCNs using persistent connections, then network bandwidth is conserved and latency is reduced, but accuracy may be compromised compared to direct GDS verification
Solution Approach 1:
The verification process is segmented into two levels: routine verification through DCNs using persistent connections for common cases, and escalated verification through direct GDS connections for discrepancy resolution. This segmentation allows the system to conserve network bandwidth for routine operations while maintaining high accuracy through selective direct verification when needed.
Solution Approach 2:
The system cushions against potential accuracy issues by implementing discrepancy detection mechanisms that identify when DCN-provided alias information may be stale or incorrect. When discrepancies are detected, the system prepares for escalated verification through GDS, ensuring accuracy is maintained without requiring continuous direct GDS connections that would consume excessive network resources.
4Reliability
If the system maintains up-to-date alias mappings through periodic verification, then data accuracy is improved, but computing resources are consumed
Solution Approach 1:
The system implements periodic alias verification at intervals optimized to balance data accuracy with computing resource consumption. Rather than continuous verification, the periodic approach maintains reliable data by checking at appropriate intervals while allowing system resources to be used for other tasks between verifications, thus managing energy consumption effectively.
Solution Approach 2:
The system efficiently manages computing resources by discarding stale alias data and recovering updated mappings only when necessary through discrepancy detection. This approach avoids continuous processing of outdated information and focuses computational effort on actual updates, improving reliability while minimizing unnecessary resource consumption.
Data Source
AI summary
Implementations described herein relate to periodically verifying aliases, e.g., by causing cross-platform clients to periodically verify aliases with cross-platform servers to which they are subscribed. In various implementations, a global discovery server (GDS) may be queried for a mapping between an alias and a connection string of an input-output (I/O) channel hosted by a distributed control node (DCN). A cross-platform client may be subscribed to the I/O channel by establishing a persistent network connection using the connection string. The persistent network connection may be used to obtain, from a local alias database of the DCN, a locally updated list of mappings between aliases and connection strings. A discrepancy may be identified between the locally updated list of mappings and the mapping between the alias and the connection string of the I/O channel hosted by the DCN. Based on the discrepancy, various remedial actions may be taken.