Industrial Prediction Model Learning for Fast Parameter Optimization
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Solution Overview
Problem
Existing methods for predicting the performance of complex industrial systems, such as intelligent robots and automatic driving systems, are inefficient due to the need for extensive simulation and real data collection, which is costly and time-consuming, and often limited to credibility evaluation rather than performance metrics.
Innovation Solution
A prediction model learning method that uses statistical metrics to quantify simulation tasks, extracts parameter groups, adjusts their values, and records performance metrics through simulations, then trains a machine learning algorithm to generate a prediction model, allowing for online prediction and parameter optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulation is performed to evaluate system performance, then prediction accuracy is improved, but simulation execution time increases significantly
Solution Approach 1:
The patent pre-generates a comprehensive dataset through simulations covering the entire design space before online prediction is needed. This preliminary simulation work creates a training dataset that captures system behavior under various conditions, enabling fast online predictions without requiring extensive real-time simulation execution.
Solution Approach 2:
The patent creates a predictive model that copies the essential behavior patterns learned from simulation data. Instead of running simulations during online operation, the system uses a trained prediction model that replicates simulation outcomes, providing accurate predictions at a fraction of the computational cost and time.
2Measurement precision
If real data is collected from all subsystems, then model accuracy is improved, but data collection cost and difficulty increase
Solution Approach 1:
The patent uses simulation-generated data as a substitute for expensive and difficult-to-collect real data from all subsystems. The simulation environment replicates system behavior and generates comprehensive datasets that mirror real-world conditions, eliminating the need for extensive physical data collection infrastructure.
Solution Approach 2:
The patent introduces simulation as an intermediary between the physical system and the prediction model. Rather than directly collecting data from complex physical subsystems, the simulation acts as a mediator that generates equivalent training data more easily and comprehensively.
3Manufacturing precision
If extensive simulation is performed to explore design space, then parameter optimization accuracy is improved, but man-labor and time requirements increase
Solution Approach 1:
The patent performs comprehensive parameter exploration and optimization through simulations in advance, before the model is deployed. The training dataset encompasses a wide range of parameter combinations and system configurations, allowing the model to learn optimal parameter settings without requiring extensive manual parameter debugging during operation.
Solution Approach 2:
The patent enables the system to automatically optimize parameters using the trained prediction model without requiring manual intervention. The model can predict performance outcomes for different parameter settings and guide self-optimization, reducing the need for human experts to manually debug and tune parameters.
Data Source
AI summary
Various embodiments include prediction model learning methods for industrial systems, including a simulation according to a simulation task on a platform. Some methods include: using statistical metrics to quantify a simulation task to extract features; extracting parameter groups from system modules, adjusting the values of the parameter groups, and triggering a simulation for the industrial system on the simulation platform based on a plurality of parameter groups having different values; recording performance metrics according to the values of the parameter groups after the parameter adjustment; and training data with a machine learning algorithm based on the features of the simulation task, the corresponding parameter groups having different values and the performance metrics, and generating a prediction model.