Functional Data Structure for Measurement System Integration
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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, especially in complex and non-standardized data processing environments, leading to information losses and increased complexity.
Innovation Solution
A method utilizing a functional data structure design that maps variables to process data, allowing for lossless vertical integration and real-time parameter optimizations, with a cloning process to manage complexity and enable organization-independent connectivity across heterogeneous interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data aggregation is performed across multiple processing layers to facilitate understanding and use, then data comprehension is improved, but information loss and misinterpretation occur
Solution Approach 1:
The patent segments data processing into distinct functional layers (data collection layer, data processing layer, data presentation layer) where each layer handles specific tasks. This segmentation allows data to be aggregated for comprehension at higher layers while preserving raw data integrity at lower layers, thus facilitating understanding without information loss.
Solution Approach 2:
The patent implements a nested data structure where aggregated data at higher processing layers contains references to and can be traced back to raw data at lower layers. This nested arrangement allows multi-level data representation where summary information coexists with detailed source data, enabling both comprehension and information preservation.
2Ease of manufacture
If standard hierarchical processing is used to integrate control systems, then implementation is simplified, but flexibility and adaptability are reduced
Solution Approach 1:
The patent introduces dynamic configuration capabilities that allow the hierarchical processing structure to adapt its parameters, data flow paths, and processing rules at runtime. This dynamic nature enables the system to maintain simplified hierarchical organization while adapting to different integration scenarios and requirements, thus preserving both implementation simplicity and system flexibility.
Solution Approach 2:
The patent creates a universal integration framework with standardized interfaces and processing mechanisms that can handle multiple types of control systems and data formats. This universal approach allows the same hierarchical structure to be applied across different scenarios, simplifying implementation while adapting to various system requirements through configuration rather than structural changes.
3Reliability
If complete integration of distributed systems is achieved, then information integrity is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary integration platform that mediates between distributed control systems. This intermediary layer handles data normalization, validation, and coordination, ensuring information integrity across systems while shielding individual systems from the complexity of full integration. The intermediary absorbs integration complexity, allowing connected systems to remain relatively simple.
4Adaptability or versatility
If data processing systems are customized to meet specific requirements, then functional adequacy is improved, but integration difficulty increases
Solution Approach 1:
The patent enables customization through parameter configuration rather than structural modification. Individual data processing systems can be adapted to specific requirements by changing processing parameters, data formats, and configuration settings within the standardized framework. This parameter-based customization maintains functional adequacy while preserving integration compatibility, avoiding the complexity of customizing system architectures.
Data Source
AI summary
A method for integration and coordination of measurement and/or control systems by means of a system that is based on a functional data structure, wherein the measurement and/or control systems to be integrated each generate or process data values for the data structure and can generate and modify data structure elements.The method comprises generating a functional data structure with variables for mapping the data values of the measurement and/or control systems;describing the content 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 networks of variables;creating a primary clone of a variable in the event that at least one of its defining variable attribute characteristics was changed by one of the integrated measurement and/or control systems; andcreating machine clones of those variables that lie on dependent variable network paths of the primary cloned variables.