Functional Data Structure Integration for Measurement Control Lineage

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Solution Overview

Problem

Current integration methods for measurement and control systems face challenges in achieving complete, synchronous, and both logical and physical integration, particularly in complex and heterogeneous environments, leading to potential loss of information and increased complexity, especially in vertical control integration where data lineage analysis is crucial.

Innovation Solution

A method utilizing a functional data structure that maps variables with attributes, allowing for dynamic integration and real-time optimization of data structures and processing events across interfaces, enabling loss-free vertical integration and comprehensive data lineage analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If data is processed through multiple consolidation layers (ETL processes), then the data becomes easier to understand and use, but information loss and misinterpretation occur

Engineering Contradiction:
Improveease of useVSAvoidinformation loss
Core Design Contradiction:
Ease of operationVSLoss of information

Solution Approach 1:

The patent segments the data processing architecture into distinct functional layers: a consolidation layer for aggregated data and a reconstruction layer for detailed data lineage. This segmentation allows each layer to serve its specific purpose without compromising the other - the consolidation layer provides ease of use while the reconstruction layer preserves complete information through maintained relationships between original and aggregated data.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent introduces a data lineage reconstruction mechanism as an intermediary between the consolidation process and the final output. This intermediary maintains and tracks the relationships between original data elements and their aggregated forms, enabling information reconstruction when needed while allowing consolidation to proceed for routine operations.

Inventive Principle:
Principle #24Intermediary (Mediator)

2Ease of manufacture

If standard integration processes are used, then integration is simplified, but flexibility and adaptability are limited

Engineering Contradiction:
Improveintegration simplicityVSAvoidflexibility
Core Design Contradiction:
Ease of manufactureVSAdaptability or versatility

Solution Approach 1:

The patent implements a dynamic integration architecture where the data structure and processing paths can be adapted based on requirements. The system allows standard consolidation processes to be used when applicable, while simultaneously enabling custom data lineage configurations and reconstruction paths to be defined for specialized needs, thus providing both simplicity and flexibility.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The patent creates a universal data processing framework that can handle both standard ETL consolidation scenarios and custom data lineage requirements through a single integrated system. The architecture supports multiple processing modes and configuration options, allowing the same system to serve diverse integration needs without requiring separate specialized solutions.

Inventive Principle:
Principle #6Universality (Multi-functionality)

3Reliability

If complete vertical integration with full data lineage is implemented, then information completeness is improved, but system complexity increases

Engineering Contradiction:
Improveinformation completenessVSAvoidsystem complexity
Core Design Contradiction:
ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies local quality by implementing data lineage tracking and reconstruction capabilities selectively where needed rather than uniformly across all data processing operations. The system provides complete vertical integration and full data lineage where information completeness is critical, while allowing simplified processing where standard consolidation suffices, thus managing complexity through targeted implementation.

Inventive Principle:
Principle #3Local quality

Solution Approach 2:

The patent performs preliminary actions by establishing data lineage relationships and metadata structures during the initial data integration phase. This preliminary setup enables complete information reconstruction when needed without adding complexity to ongoing processing operations, as the foundational relationships are already in place and can be leveraged on-demand.

Inventive Principle:
Principle #10Preliminary action

4Adaptability or versatility

If distributed measurement and control systems are integrated, then system coverage and functionality are improved, but coordination complexity increases

Engineering Contradiction:
Improvesystem coverageVSAvoidcoordination complexity
Core Design Contradiction:
Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent merges multiple distributed measurement and control systems into a unified data processing architecture that maintains their individual functionalities while coordinating them through a common consolidation and reconstruction framework. This merging approach preserves the coverage and adaptability benefits of distributed systems while reducing coordination complexity through standardized interfaces and centralized data lineage management.

Inventive Principle:
Principle #5Merging (Combining)

Data Source

PatentEP3896579A1Method for integration and coordination of measurement and / or control systems
Publication Date: 2021.10.20 GLUECK THOMAS ROBERT
  • EP3896579A1 patent drawingFigure 1
  • EP3896579A1 patent drawingFigure 2a
  • EP3896579A1 patent drawingFigure 2b

AI summary

The invention relates to a method for integrating and coordinating measurement and/or control systems by means of a system based on a functional data structure, wherein the measurement and/or control systems to be integrated can each generate or process data values ​​for the data structure, as well as create and modify data structure elements, comprising the steps: a. generation of a functional data structure with variables for mapping the data values ​​of the measurement and/or control systems, b. content description of the variables by means of a set of defining attributes, wherein at least one attribute can contain variable references to other variables in order to map variable networks, c. creation of a primary clone of a variable if at least one of its defining variable attribute values ​​has been changed by one of the integrated measurement and/or control systems, d.Generating machine clones of those variables that lie on dependent variable network paths of the primary cloned variable.