IIoT Cloud Process Control for Personalized Production Adjustment
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Solution Overview
Problem
The manufacturing industry faces challenges in effectively utilizing data from production lines to optimize processes due to a lack of intelligent and personalized recommendation mechanisms, leading to inefficiencies and suboptimal production quality.
Innovation Solution
A system and method utilizing Industrial Internet of Things (IIoT) information cloud sharing, which includes a cloud platform connected to multiple IIoT systems, uses sensors to collect data, and employs machine learning algorithms to generate personalized production process recommendations, adjusting operating parameters of production line equipment through a control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual experience or fixed processes are used for production process selection, then implementation is simple, but production efficiency and product quality cannot be optimized effectively
Solution Approach 1:
The patent introduces a cloud platform as an intermediary between production equipment and the control system. This cloud platform collects data from multiple factories through IIoT systems, processes it using machine learning models, and generates optimized production process recommendations. This intermediary approach enables intelligent optimization without requiring complex local computing resources at each factory, thus improving productivity while keeping individual factory systems relatively simple.
Solution Approach 2:
The patent replaces manual experience-based decision-making with automated machine learning models. The system uses trained models to analyze production data and automatically generate optimized production process parameters, substituting the mechanical process of manual analysis and decision-making with an intelligent automated system that can process data more efficiently and accurately.
2Adaptability or versatility
If fixed processes are used for production, then process control is simple, but personalized recommendations cannot be provided
Solution Approach 1:
The patent utilizes parameter changes in the machine learning models to achieve adaptability. The system trains different models or adjusts model parameters based on specific factory characteristics, product types, and production requirements. This allows the system to provide personalized recommendations for different scenarios while maintaining a unified platform architecture, balancing adaptability with system complexity.
Solution Approach 2:
The patent segments the production process into multiple controllable parameters (e.g., screening parameters, conveying parameters, assembly parameters, quality detection parameters). By independently optimizing each parameter based on data analysis, the system can provide personalized recommendations without requiring complete process redesign, thus achieving adaptability while managing system complexity.
3Manufacturing precision
If production data is not utilized effectively, then data collection is simple, but process optimization cannot be achieved
Solution Approach 1:
The patent implements a feedback mechanism where production data is continuously collected from the production line, analyzed by machine learning models on the cloud platform, and used to generate optimized production process recommendations. These recommendations are then applied to adjust production parameters, and the results are fed back into the system for continuous improvement. This closed-loop feedback system ensures that data is effectively utilized to continuously enhance product quality and production efficiency.
Data Source
AI summary
Method and system for intelligent recommendation of a production process by Industrial Internet of Things (IIOT) information cloud sharing are provided. The method includes: obtaining and storing production data of a production line; determining, based on the production data, whether an operating parameter of the production line equipment needs to be adjusted; in response to a determination that the operating parameter of the production line equipment needs to be adjusted, generating, based on the production data, a production process parameter and an adjust time; generating, based on the production process parameter and the adjust time, a process adjustment instruction and issuing the process adjustment instruction to the IIOT management platform; analyzing the process adjustment instruction, and regulating the operating parameter of the production line equipment based on the process adjustment instruction when the adjust time is reached.