Industrial Control Data Reading Cycles from Simulated Data Features
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The configuration of data reading cycles in industrial control systems is typically manual and experience-dependent, leading to suboptimal results that are time-consuming and labor-intensive, and can result in either insufficient data or excessive resource utilization.
Innovation Solution
An apparatus and method that automatically determines data reading cycles by simulating the industrial control system, extracting data features, grouping data, and analyzing timing information to configure optimal data reading cycles that meet the requirements of specific industrial applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the data reading cycle is set too long, then the resource utilization is reduced, but the amount of data read becomes insufficient
Solution Approach 1:
The patent implements dynamic adjustment of data reading cycles by analyzing historical data characteristics, correlation relationships, and timing information. The system automatically optimizes reading cycles for different data points based on their specific features rather than using a fixed cycle, thereby reducing overall resource utilization while ensuring sufficient data collection for each data point's characteristics.
Solution Approach 2:
The system changes the parameter of data reading cycle based on analyzed data features, correlation coefficients, and timing relationships. By adjusting the reading cycle parameter dynamically according to actual data characteristics, the system achieves optimal balance between resource efficiency and data sufficiency.
2Loss of information
If the data reading cycle is set too short, then the amount of data read is increased, but the occupied storage resources, computing resources, and transmission resources become too large
Solution Approach 1:
The system dynamically determines optimal reading cycles by analyzing data characteristics, correlations, and timing information. This dynamic approach prevents excessive data collection that would burden storage, computing, and transmission resources while still gathering sufficient data for effective analysis.
Solution Approach 2:
The patent applies partial action by reading data at optimized cycles rather than continuous or fixed frequent intervals. By reading only the necessary amount of data at appropriate intervals based on analysis, the system avoids excessive resource consumption while maintaining data sufficiency.
3Ease of operation
If manual configuration by field engineers is used, then flexibility in configuration is achieved, but the configuration process becomes time consuming and labor intensive
Solution Approach 1:
The system performs self-service by automatically analyzing data characteristics, determining correlations, and optimizing reading cycles without requiring manual field engineer configuration. The automated analysis and determination process eliminates time-consuming manual work while maintaining optimal configuration results.
Solution Approach 2:
The patent replaces the mechanical manual configuration process with an automated computational system that analyzes data features, calculates correlations, and determines optimal reading cycles algorithmically. This substitution eliminates manual labor and time consumption associated with expert-based configuration.
4Adaptability or versatility
If manual configuration by field engineers is used, then configuration can be adjusted based on experience, but the configuration result becomes suboptimal
Solution Approach 1:
The system uses feedback from data analysis, correlation calculations, and timing information to continuously optimize reading cycle configurations. By incorporating feedback from actual data characteristics and relationships, the system achieves superior configuration optimality that exceeds manual expert configuration capabilities.
Solution Approach 2:
The patent replaces subjective expert judgment with objective computational analysis that systematically evaluates data characteristics, correlations, and timing relationships. This substitution achieves more precise and optimal configuration results by relying on data-driven analysis rather than limited human experience.
Data Source
Figure 1
Figure 2
Figure 3~5
AI summary
The present invention relates to the field of industrial automation, and in particular, to a method and device for determining a data reading period for determining a data reading period of data in an industrial control system, and capable of automatically configuring the data reading period to obtain a better configuration result. In embodiments of the present invention, an industrial control system in different states is simulated by using simulation software to obtain a simulation model and simulation data. Data features of the industrial control system in different states that is simulated can be extracted respectively, and a data reading period is determined according to the extracted data features. Automatically configuring a data reading period is implemented.