Robot ML Model Permission Control and Operation Traceability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies do not adequately manage machine learning model training data, risking unauthorized copying and use, which can lead to loss of business opportunities.
Innovation Solution
A machine learning model operation management system comprising a model building server, robot controller, management server, and operation server, where the robot controller inquires permission to use the trained model and operation information is stored without sensitive data, ensuring secure management and reducing construction and running costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data and learned model data are stored and managed without permission control, then the system is simple and easy to operate, but security is compromised and unauthorized copying/use can occur
Solution Approach 1:
The system divides the management function into separate components: the robot controller stores and manages trained models locally, while the operation server handles operation information. This segmentation allows security controls to be applied specifically to model access without complicating the entire system.
Solution Approach 2:
The robot controller performs preliminary permission verification by inquiring the operation server before executing a trained model. This preliminary action prevents unauthorized use before it occurs, ensuring security without requiring complex real-time monitoring throughout the system.
2Loss of information
If operation information includes detailed sensitive data, then complete operation records are maintained, but construction and running costs increase due to security requirements
Solution Approach 1:
The system extracts and removes sensitive information from operation information before storing it on the operation server. Only non-sensitive operation data is transmitted and stored, maintaining operational monitoring capabilities while eliminating the need for expensive security infrastructure to protect sensitive data.
3Reliability
If permission verification is not implemented, then the robot controller can freely use trained models, but unauthorized copying and use occurs leading to loss of business opportunities
Solution Approach 1:
The robot controller automatically inquires the operation server for permission before using a trained model and receives an immediate response. This feedback mechanism ensures security through permission verification while maintaining ease of operation, as the process is automated and requires no manual intervention from the user.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A machine learning model operation management system includes a model building server and an operation server. The model building server builds a trained machine learning model based on received training data. When the trained machine learning model stored in a robot controller operates for determining the operation of a robot, the operation server receives operation information generated by the robot controller. Data of the trained machine learning model built by the model building server is assigned with model identification information that uniquely identifies the trained machine learning model. The robot controller makes an external inquiry as to whether or not it has permission to use the trained machine learning model stored in itself, and if it has the permission to use, it makes the trained machine learning model available for use. The operation server stores the operation information in association with the model identification information.