Cross-Discipline Data Validation for Engineering Consistency
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Solution Overview
Problem
In multidisciplinary engineering systems, manual synchronization of discipline-specific data across different engineering disciplines is time-consuming and error-prone, leading to inconsistencies that can impact the quality of work among engineers and technicians.
Innovation Solution
Implementing a method for cross-discipline data validation checking using predefined multidisciplinary validation rules that reference application objects across various engineering applications, allowing for automated consistency checks and warnings of potential errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual synchronization of discipline-specific data is used, then data can be exchanged between engineering disciplines, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs preliminary validation checks on data before it is synchronized between disciplines. Validation rules are defined in advance to check data consistency, completeness, and format requirements. This preliminary action prevents erroneous data from being propagated across disciplines, eliminating the need for time-consuming manual verification and correction later in the process.
Solution Approach 2:
The patent introduces a multidisciplinary validation system as an intermediary layer between different engineering disciplines. This intermediary automatically validates data according to predefined rules before data exchange occurs, serving as a mediator that ensures data quality without requiring manual intervention from engineers. The intermediary handles the synchronization process automatically, reducing both time loss and errors.
2Reliability
If manual data synchronization is performed, then data can be shared across disciplines, but human errors are introduced during the process
Solution Approach 1:
The validation system operates autonomously without requiring manual intervention. Predefined validation rules automatically check data consistency, completeness, and format requirements across disciplines. The system self-validates data before synchronization, eliminating human errors entirely while maintaining high data accuracy. This self-service approach maximizes automation while ensuring reliable data exchange.
3Reliability
If cross-discipline validation checking is implemented, then data consistency is improved, but system complexity increases
Solution Approach 1:
The validation system is segmented into modular components: predefined validation rules, data validation engine, and result reporting mechanisms. Each discipline can have its own specific validation rules while sharing a common validation framework. This segmentation allows the system to handle complex multidisciplinary validation requirements without becoming unmanageably complex, as each validation rule can be independently configured and maintained.
Data Source
AI summary
The preferred embodiments described below include methods, systems and computer readable media for cross discipline data validation checking in a multidisciplinary system. One or more multidisciplinary validation rules are used to perform cross discipline data validation checking to determine whether multidisciplinary data is consistent across engineering disciplines. The multidisciplinary validation rules define the scope of the validation checking within the engineering application (307) associated each engineering discipline. The results of the validation check are provided to the user.