Feedback Loop State Control for Reliable Data Model Operation
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Solution Overview
Problem
Complex data-analytic systems face challenges in maintaining predictive accuracy and reliability due to improper operation of data models, leading to potential system failures and costly errors, such as foaming events in petrochemical facilities or missed opportunities in retail customer decisions.
Innovation Solution
A feedback loop driven end-to-end state control system that monitors and verifies data models and contingency models, performs accuracy checks, and takes corrective actions like retraining or replacement to ensure operational reliability, using model execution logic and accuracy analysis logic to prevent out-of-tolerance operations and maintain continuous data model operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data models are used for predictive analytics in complex systems, then predictive accuracy and business value are improved, but system reliability deteriorates due to improper model operation and potential failures
Solution Approach 1:
The patent implements a feedback loop that continuously monitors data model operations and system state. When improper operation is detected (such as model drift or performance degradation), the feedback mechanism triggers corrective actions including automated retraining or replacement of data models, thereby maintaining both predictive accuracy and system reliability simultaneously
Solution Approach 2:
The system performs preliminary verification and validation of data models before deployment and operation. Contingency data models are pre-prepared and validated in advance, so when primary models show signs of improper operation, ready-to-use alternatives can immediately take over, preventing system failures while maintaining predictive capabilities
2Reliability
If continuous monitoring and verification of data models is implemented, then system reliability is improved, but device complexity increases due to additional monitoring and control mechanisms
Solution Approach 1:
The monitoring and verification system is designed to operate autonomously without requiring external intervention. The feedback loop automatically detects improper model operation, triggers retraining or replacement actions, and manages the entire lifecycle of data models self-service style, reducing the operational complexity despite enhanced monitoring capabilities
Solution Approach 2:
The system employs universal monitoring and control mechanisms that can apply to multiple different data models and analytical workflows. The same feedback loop infrastructure serves various predictive analytics functions, reducing overall system complexity through multi-functionality rather than requiring separate monitoring systems for each model
3Measurement precision
If automated retraining and replacement of data models is performed, then predictive accuracy is maintained, but loss of time increases due to model retraining and state transitions
Solution Approach 1:
Contingency data models are pre-trained and validated in advance before they are needed. When the primary data model shows signs of improper operation, the pre-prepared contingency model can immediately take over without requiring time-consuming retraining, thus maintaining predictive accuracy while minimizing time loss
Solution Approach 2:
The system implements periodic verification and accuracy checks of data models at scheduled intervals. This proactive approach allows for planned retraining during off-peak periods rather than emergency retraining when model failure occurs, reducing the impact on operational time while maintaining predictive accuracy
Data Source
AI summary
A system provides feedback driven end-to-end state control of a data model. A data model may be used to model the behavior of a petrochemical refinery to predict future events. The system may be used to ensure proper operation of the data model. Contingency data models may be executed when a failure is detected. Further, when the system detects accuracy that is out of tolerance, the system may initiate retraining of the data model being currently used.