Control Development Data Commitment Using Dependency Rulesets
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Solution Overview
Problem
Current data management systems for control devices are labor-intensive and prone to errors due to the complexity of modeling comprehensive development data, requiring manual management of numerous datasets and version control, which is time-consuming and difficult to scale effectively.
Innovation Solution
A semi-automated method for development data management that involves saving a development data model in a central data store with related configuration data units, using a ruleset to identify and automatically select a subset of configuration data units based on relationships, allowing for efficient management and versioning of data without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual management of comprehensive development data is used, then data completeness can be achieved, but labor intensity and error susceptibility increase significantly
Solution Approach 1:
The system enables self-service through automated dependency resolution. When a user commits a selected data object, the system automatically identifies and commits all dependent objects based on pre-defined relationships in the semantic network, eliminating the need for manual specification of each dependent object while ensuring data completeness.
Solution Approach 2:
The patent introduces an intermediary layer (the data management system with semantic networks) between the user and the comprehensive data set. This intermediary automatically resolves dependencies and manages data relationships, reducing manual effort while maintaining completeness of data commitment.
2Quantity of substance
If all dependent objects are committed in a blanket manner, then data completeness is ensured, but unnecessary data commitment increases storage and processing overhead
Solution Approach 1:
The system applies local quality by allowing users to select specific data objects for commitment and then automatically applying dependency rules only to the relevant subset. This ensures that only necessary dependent objects are committed, avoiding blanket commitment of all objects while maintaining local data completeness.
Solution Approach 2:
The patent segments the comprehensive data set into individual data objects with defined relationships. This segmentation allows selective commitment of specific objects and their necessary dependencies, rather than committing the entire data set, thereby reducing storage overhead while maintaining necessary completeness.
3Adaptability or versatility
If manual specification of dependent objects is required, then flexibility in selection is achieved, but time consumption and complexity increase
Solution Approach 1:
The system performs preliminary action by pre-defining data relationships and dependencies in semantic networks before the commitment process. When a user initiates a commit, the system has already prepared the dependency information, enabling rapid automatic resolution without manual specification during the actual commitment operation.
Solution Approach 2:
The patent implements feedback mechanisms where the system presents the user with automatically identified dependent objects for confirmation or modification. This feedback loop maintains user control and flexibility while significantly reducing the time required compared to manual specification of all dependencies from scratch.
4Measurement precision
If comprehensive data models are created for control devices, then testing accuracy is improved, but model complexity and error susceptibility increase
Solution Approach 1:
The patent segments comprehensive data models into discrete, manageable data objects with well-defined relationships. This segmentation maintains testing accuracy by preserving the completeness of the data model while reducing complexity through modular organization and automated dependency management.
Solution Approach 2:
The system provides self-service through automated dependency resolution and version management, reducing the complexity burden on users. The automated system handles the complex tasks of tracking dependencies and managing versions, allowing users to work with comprehensive models without being overwhelmed by their complexity.
Data Source
AI summary
A method for semi-automated development data management for control devices includes saving a development data model in a central data store comprising a plurality of mutually related configuration data units, wherein the configuration data units each store control commands and/or configuration parameters. The method further includes providing a ruleset and identifying an initial configuration data unit, wherein it is possible, using the ruleset, to identify further configuration data units automatically on a basis of a relationship thereof with the initial configuration data unit. In addition, the method includes applying the provided ruleset to the development data model in order to identify a subset of configuration data units within the development data model and saving the identified subset.