Control Loop Grading and Data Labeling for Self-Evaluation
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Solution Overview
Problem
Traditional automation approaches lack the ability to learn and evaluate their performance, requiring manual and costly efforts for performance measurements, which are often ineffective due to limited scope and lack of flexibility, and insufficient data collection for machine learning.
Innovation Solution
An automated control loop grading and data labeling system that analyzes and grades control loop performance, collects and labels data based on predefined policies, enabling continuous improvement and flexibility in data collection and usage for machine learning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual performance measurements are conducted, then performance evaluation is possible, but it is expensive and difficult
Solution Approach 1:
The control loop system automatically performs performance measurements and data collection without requiring manual setup. The system self-evaluates its own performance by continuously monitoring execution results and automatically collecting relevant data, eliminating the need for human operators to configure measurement tools and reducing both cost and complexity.
Solution Approach 2:
The system implements continuous feedback loops where performance data is automatically collected, analyzed, and used to improve future executions. This automated feedback mechanism enables ongoing performance evaluation without manual intervention, creating a self-improving system that reduces operational complexity while maintaining measurement precision.
2Measurement precision
If manual performance measurements are conducted, then performance evaluation is possible, but it requires considerable additional effort and planning
Solution Approach 1:
The system pre-configures performance measurement capabilities and data collection mechanisms as integral components of the control loop architecture. By establishing these measurement infrastructures in advance as part of the system design, the need for time-consuming manual setup and planning is eliminated, allowing immediate performance evaluation upon deployment.
Solution Approach 2:
The control loop system automatically performs performance measurements and data collection without requiring manual setup. The system self-evaluates its own performance by continuously monitoring execution results and automatically collecting relevant data, eliminating the need for human operators to configure measurement tools and reducing both cost and complexity.
3Extent of automation
If traditional automation approaches are used, then automation is achieved, but the system cannot learn or evaluate itself
Solution Approach 1:
The system implements continuous feedback loops where performance data is automatically collected, analyzed, and used to improve future executions. This automated feedback mechanism enables ongoing performance evaluation without manual intervention, creating a self-improving system that reduces operational complexity.
Solution Approach 2:
The control loop system automatically performs performance measurements and data collection without requiring manual setup. The system self-evaluates its own performance by continuously monitoring execution results and automatically collecting relevant data, eliminating the need for human operators to configure measurement tools and reducing both cost and complexity.
4Quantity of substance
If manual data collection is performed, then data is obtained, but it is insufficient for machine learning
Solution Approach 1:
The system continuously collects performance data during every control loop execution rather than performing periodic manual data collection. This continuous automated data gathering ensures sufficient data volume for machine learning applications while maintaining high productivity, as the data collection occurs naturally during system operation without interrupting or slowing down the control loop executions.
Data Source
AI summary
Concepts and technologies disclosed herein are directed to automated control loop grading and data labeling (“ACLGDL”). An ACLGDL system analyzes results of an execution, by a control loop system, of a control loop. The ACLGDL system can grade the results. The ACLGDL system also can instruct, based at least in part upon the grade of the results of the execution, the control loop system to collect additional data. The ACLGDL system can label the additional data for use by an output system. The ACLGDL system can establish plurality of policies including a grading-analysis policy, a grading-results policy, a labeling-collection policy, a labeling policy, a publishing policy, and a notification policy. The ACLGDL system can publish the data labeled in accordance with the labeling policy based, at least in part, upon the publishing policy. The ACLGDL system can notify the output system based, at least in part, upon the notification policy.