AI Digital Engineering Documentation With Zero-Trust Data Access
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Solution Overview
Problem
Current digital engineering documentation processes are inefficient, error-prone, and costly, with manual documentation leading to redundant physical tests, high archiving costs, and security risks due to the sharing of sensitive engineering data.
Innovation Solution
The integration of artificial intelligence (AI) and machine learning (ML) to assist in documentation, along with zero-trust access control, ensures seamless digital documentation while maintaining data security, and the use of live digital engineering (DE) documents that are perpetually updated to reflect the latest changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual documentation processes are used, then flexibility and ease of operation are maintained, but productivity is low and errors increase
Solution Approach 1:
The system enables self-service documentation generation where the digital engineering system automatically creates, updates, and manages documentation without requiring manual intervention. The system extracts data from digital models and automatically populates documentation templates, reducing manual workload while maintaining documentation quality and consistency.
Solution Approach 2:
Manual documentation processes are replaced with an automated digital system that uses software algorithms to generate documentation. The system substitutes human manual operations with automated computational processes that extract data from digital engineering models and generate documentation outputs, significantly improving productivity and reducing errors.
2Reliability
If sensitive engineering data is shared for documentation, then comprehensive documentation is achieved, but security risks increase
Solution Approach 1:
The system introduces an intermediary layer between the sensitive engineering data and the documentation output. This intermediary process selectively extracts and transforms data, providing only the necessary information for documentation while filtering out sensitive details. The system acts as a mediator that ensures documentation completeness without exposing unnecessary sensitive data.
Solution Approach 2:
The system applies different data access and exposure levels to different parts of the engineering data. Sensitive portions of data are protected and not exposed in documentation, while non-sensitive portions are freely used. The system selectively manages data quality and exposure based on the specific requirements of each documentation section, ensuring both completeness and security.
3Reliability
If physical tests are repeated to ensure documentation accuracy, then reliability is improved, but loss of time and resources increases
Solution Approach 1:
The system creates digital copies of test data and model information that can be used to generate documentation without requiring physical replication of tests. Instead of repeating physical tests, the system extracts and copies relevant data from existing digital models and test results, maintaining documentation accuracy while eliminating redundant physical testing and associated time losses.
4Productivity
If digital documentation systems are implemented, then productivity and accuracy are improved, but device complexity increases
Solution Approach 1:
The system is designed as a universal platform that handles multiple documentation tasks and integrates with various digital engineering tools through standardized interfaces. By creating a multi-functional system that can manage different types of documentation and connect to diverse data sources, the system achieves high productivity without proportionally increasing complexity, as the same core infrastructure serves multiple purposes.
Data Source
AI summary
A digital documentation system for preparation of engineering documents utilizing one or more artificial intelligence (AI) algorithms is provided. The system includes a user interface for selecting and populating templates with data, and one or more AI algorithms for creating and recommending templates, and preparing documents based on the recommended templates. The system uses natural language processing and semantic analysis algorithms to understand the content of the templates, documents, and associated engineering data, and to generate and recommend relevant templates to the user based on user prompts. The system also uses machine learning and predictive modeling and decision-tree algorithms to assist with the preparation of documents, by generating suggestions for data fields and values based on the user's previous inputs and the overall context of the document and available engineering data, including model data and metadata from digital models accessed in a zero-trust framework.