Manufacturing Data Organization via Shared Channel Boundaries
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Solution Overview
Problem
Existing methods for capturing, organizing, and analyzing manufacturing process information on resource-constrained embedded devices face challenges such as memory limitations, reliance on network reliability, redundancy, complexity in record relationships, and issues with timestamps, which hinder efficient storage and analysis of manufacturing data.
Innovation Solution
The method organizes information into extensible Channels with Events and Event Fragments, using Channel Boundary Relationships to create shared Boundaries, allowing for efficient storage, analysis, and modification of data, while utilizing time as a piece of information rather than a key to relate records, thereby reducing redundancy and improving query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If information is stored in a back-end database on a server-class computer or cloud storage, then long-term storage capacity is improved, but network reliability dependency increases and system self-containment decreases
Solution Approach 1:
The patent segments the storage system into multiple hierarchical levels: embedded device local storage, edge server storage, and cloud storage. This segmentation allows the system to maintain data locally for reliability while enabling optional synchronization with external storage systems, thus resolving the contradiction between storage capacity and network dependency.
Solution Approach 2:
The embedded device is designed to independently capture, organize, and store manufacturing process information in its local database without requiring network connectivity. This self-service capability ensures reliability during network outages while maintaining the option to synchronize data when connectivity is available, addressing both storage capacity and reliability concerns.
2Loss of information
If full information records are stored for each span of time and aspect of the process, then information completeness is improved, but memory usage increases and redundancy increases
Solution Approach 1:
The patent extracts only the essential and non-redundant information elements needed for manufacturing process analysis. By identifying and storing only critical process parameters, events, and metrics rather than complete information records, the system maintains information completeness for analytical purposes while significantly reducing memory usage and eliminating redundancy.
Solution Approach 2:
Instead of storing complete records and filtering later, the patent inverts the approach by designing the data capture stage to collect only essential information from the beginning. This inversion prevents redundancy at the source while maintaining all information needed for comprehensive process analysis, thus resolving the contradiction between information completeness and memory efficiency.
3Loss of time
If timestamps are used to relate data records, then chronological ordering is improved, but timestamp reliability issues increase and complexity increases
Solution Approach 1:
The patent introduces an intermediary mechanism - a centralized time synchronization system using NTP (Network Time Protocol) - to mediate timestamp generation across all devices. This intermediary ensures that all embedded devices, edge servers, and cloud systems use a unified, reliable time source, thereby maintaining chronological ordering accuracy while eliminating timestamp reliability issues through centralized time management.
4Quantity of substance
If information is aggregated at the time it is captured, then storage efficiency is improved, but analysis capability decreases and information detail is lost
Solution Approach 1:
The patent implements a dynamic data organization structure where information is captured in detail and can be aggregated at multiple hierarchical levels based on analytical needs. The system dynamically adjusts the level of aggregation - storing detailed event data for recent processes while maintaining aggregated summaries for historical analysis - thus improving storage efficiency without permanently losing analysis capability through premature aggregation.
Data Source
AI summary
A method and system for capturing, organizing, storing, and analyzing manufacturing process information, with storage and performance characteristics suitable for use in resource-constrained embedded devices. Process information is organized into extensible Channels, each of which captures information stored as extensible Events, each of which may include Metric, Category Value, Annotation, and System Fields. Shared Boundaries between Channels make it easier to organize, store, interrelate, analyze, and explore information. Channel Boundary Relationships (e.g., Coupling, Fragmenting, Projecting, and Aggregating) create shared Boundaries; and can create, or be combined to create, sets of Channels that share Boundaries (referred to as a Slice Set). Shared Boundaries and Channel Boundary Relationships facilitate rule-based traversal of Exploration Hierarchies constructed from Channels, Categories, and Metrics, which enable users to view, analyze, interpret, understand, and otherwise benefit from Metrics and other information while traversing groups of Categories.