IIoT Process Recommendation for Adaptive Production Parameter Control
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Solution Overview
Problem
The manufacturing industry faces challenges in effectively utilizing the vast amount of data generated by production lines to optimize production processes, leading to inefficiencies and suboptimal product quality due to reliance on manual experience or fixed processes, lacking intelligent and personalized recommendations.
Innovation Solution
A method and system for intelligent production process recommendation through Industrial Internet of Things (IIoT) information cloud sharing, utilizing a cloud platform that integrates data from multiple factories, processes production data to determine parameter adjustments, and generates personalized process adjustment instructions for production line equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual experience or fixed processes are used for production process selection, then implementation simplicity is maintained, but production efficiency and product quality cannot be optimized effectively
Solution Approach 1:
The patent introduces an AI recommendation system as an intermediary between the existing production systems and the decision-making process. This system processes production data from multiple factories and provides intelligent recommendations for process parameter adjustments, enabling optimization without requiring complete system restructuring or high complexity implementation
Solution Approach 2:
The system enables production lines to automatically adjust their own parameters based on AI-generated recommendations. The production equipment self-regulates operating parameters (temperature, pressure, speed, etc.) according to the recommendation instructions, reducing the need for manual intervention while maintaining operational simplicity
2Adaptability or versatility
If fixed processes are used for production, then process stability is maintained, but adaptability to complex and changing production demands is reduced
Solution Approach 1:
The patent transforms fixed production processes into dynamic, adjustable processes. The system continuously receives production data from multiple factories, generates real-time recommendations for parameter adjustments, and enables production equipment to dynamically adapt their operating parameters (temperature, pressure, speed, etc.) to meet changing production demands while maintaining product quality
Solution Approach 2:
The system implements a closed-loop feedback mechanism where production data from multiple factories is continuously collected, analyzed by AI algorithms, and used to generate recommendation instructions that are fed back to production equipment. This feedback loop enables the system to adapt to changing conditions while maintaining manufacturing precision through data-driven parameter optimization
3Loss of information
If production data from multiple factories is collected and analyzed, then intelligent recommendations can be generated, but data processing complexity and time consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing production data from multiple factories in advance. The AI model is trained on historical data beforehand, enabling it to quickly generate recommendations when new data arrives, thus reducing real-time processing time while maximizing data utilization
Solution Approach 2:
The patent segments the data processing task into manageable components: data collection from multiple factories, data preprocessing and cleaning, AI model training, recommendation generation, and implementation feedback. This segmentation allows parallel processing of different data streams and reduces overall processing time while comprehensively utilizing production data
Data Source
AI summary
Disclosed is method and system for intelligent recommendation of a production process by Industrial Internet of Things (IIoT) information cloud sharing. The method includes: obtaining and storing production data of the 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; and 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 instructions, and regulating the operating parameter of the production line equipment based on the process adjustment instruction when the adjust time is reached.