Industrial Prediction Model Learning for Fast Parameter Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting the performance of complex industrial systems, such as intelligent robots and automatic driving systems, are inefficient and costly, often requiring extensive simulations and real data collection, which is expensive and impractical, and lack effective tools for online performance prediction and parameter optimization.
Innovation Solution
A simulation-based prediction model learning method that uses statistical metrics to quantify simulation tasks, extracts parameter groups, adjusts their values, records performance metrics, and employs machine learning to generate a prediction model, allowing for automatic parameter adjustment and online performance prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a prediction model is constructed using all available process data and sensor data from an industrial system, then the prediction accuracy is improved, but the learning time and computational resources required increase significantly
Solution Approach 1:
The patent segments the large dataset into multiple subsets and divides the learning process into multiple stages. In each stage, different subsets of process data and sensor data are used to train the prediction model iteratively. This segmentation allows the system to achieve high prediction accuracy through progressive learning while reducing the computational burden and learning time compared to training on the complete dataset at once.
Solution Approach 2:
The patent performs preliminary data processing and feature selection before the main prediction task. Relevant features are identified and extracted from the available process data and sensor data in advance, and the data is preprocessed to remove redundancies. This preliminary action reduces the dimensionality of the input data, enabling faster model training while maintaining prediction accuracy.
2Reliability
If more process data and sensor data are collected and used for model training, then the prediction reliability is improved, but the complexity of the system increases
Solution Approach 1:
The prediction model is designed with universal applicability to handle multiple types of data sources (process data from process control systems and sensor data from distributed control systems) and various prediction tasks. The same model architecture and learning mechanism can process different data formats and perform different predictions, reducing the need for separate specialized systems for each data source or task, thereby maintaining prediction reliability while controlling system complexity.
Solution Approach 2:
The patent dynamically adjusts model parameters such as the number of neurons in hidden layers, learning rates, and regularization coefficients based on the characteristics of the input data. This parameter adaptation allows the model to optimize its performance for different data configurations without requiring a complete redesign of the system architecture, thus maintaining high prediction reliability while avoiding excessive system complexity.
3Adaptability or versatility
If the prediction model uses comprehensive data from multiple sources including process control systems and distributed control systems, then the prediction capability is improved, but the data integration and processing complexity increases
Solution Approach 1:
The patent introduces an intermediary data processing layer that receives data from multiple sources (process control systems and distributed control systems) and standardizes it into a unified format before feeding it to the prediction model. This intermediary layer performs data validation, format conversion, and feature extraction, enabling the model to effectively utilize comprehensive data from diverse sources while shielding the model from the complexity of data integration and processing.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Provided are a prediction model learning method, apparatus and system for an industrial system, wherein by means of the industrial system, simulation is executed according to a simulations task on a simulation platform. The prediction model learning method for an industrial system comprises the following steps: S1, using a statistical index to quantify a simulation task of the industrial system so as to extract features of the simulation task; S2, extracting a parameter group from a system modul, which executes simulation, of the industrial system, adjusting the numerical value of the parameter group, and triggering a simulation platform to execute simulation on the industrial system on the basis of a plurality of parameter groups of different numerical values; S3, recording a performance index according to the numerical values of the parameter groups subjected to parameter adjustment; and S4, on the basis of the features of the simulation task and the corresponding parameter groups of different numerical values and performance indexes thereof, training data by means of a machine learning algorithm, and generating a prediction model. By using the prediction model, a user can quickly predict the system performance based on a specific scenario and parameters, and optimize the system by means of automatically selecting the system parameters.