Industrial Visual Content Provisioning for Faster Process Monitoring
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Solution Overview
Problem
Industrial control systems face challenges in providing visual content to users in a timely manner due to heavy processing loads, leading to increased call-up times, which hinders user experience and efficiency in monitoring industrial processes.
Innovation Solution
A method and control system utilizing a machine learning model to identify critical variables and user behavior data to dynamically provision a reduced set of visual contents, prioritizing critical variables and relevant user data, and caching these contents for quick access, thereby reducing call-up times and enhancing user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the control system provides all visual contents to users, then complete process monitoring information is available, but call-up time increases due to heavy processing load
Solution Approach 1:
The system extracts and provides only the most relevant visual contents to users based on machine learning analysis of user roles, preferences, and critical process variables. Instead of providing all available visual contents, the system selectively extracts and delivers only those that are most important for each user's monitoring needs, thereby reducing call-up time while maintaining essential information availability.
Solution Approach 2:
The system applies local quality by customizing the set of visual contents provided to each user based on their specific role, preferences, and the criticality of processes they monitor. Different users receive different subsets of visual contents tailored to their local needs, rather than a uniform comprehensive set, optimizing both information relevance and retrieval speed.
2Reliability
If the control system provides comprehensive visual contents, then all process variables are monitored, but user experience deteriorates due to delayed content delivery
Solution Approach 1:
The system extracts and prioritizes critical visual contents based on machine learning analysis of user roles and preferences. By providing only the most essential visual contents first, the system maintains reliable process monitoring coverage while delivering content quickly, thus improving user experience without sacrificing monitoring reliability.
Solution Approach 2:
The system performs preliminary analysis using machine learning to predict which visual contents each user will need before requests are made. This preliminary action allows the system to pre-identify and prioritize critical visual contents, ensuring that reliable monitoring information is delivered quickly when users access the system.
3Measurement precision
If the control system processes all visual contents, then complete data analysis is achieved, but processing time increases significantly
Solution Approach 1:
The system extracts only the most relevant visual contents for processing and delivery based on machine learning analysis of user needs and critical process variables. By extracting and processing only essential contents rather than all available visual contents, the system maintains data analysis precision for critical parameters while significantly improving content delivery speed and overall productivity.
Solution Approach 2:
The system applies partial action by processing and delivering a subset of visual contents that are most relevant to each user's role and preferences. Rather than processing all visual contents equally, the system focuses computational resources on partial processing of critical contents, achieving sufficient analysis precision for operational needs while improving delivery speed.
Data Source
AI summary
A method for providing visual contents to user for monitoring processes in an industrial system. The method comprises receiving plurality of variables associated with processes in industrial system. Further, the method comprises determining presence of one or more critical variables based on one or more parameters, using machine learning model. Furthermore, the method comprises identifying one or more first visual contents by associating the one or more critical variables with plurality of visual contents. Each of the plurality of visual contents represents one or more processes from the plurality of processes and corresponding variables. Moreover, the method comprises identifying one or more second visual contents based on availability of behaviour data of user, using the machine learning model. Thereafter, the method comprises providing the one or more first visual contents and the one or more second visual contents to the user.