IIoT Assembly Scheme Adjustment for Real-Time Line Synchronization
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Solution Overview
Problem
Traditional manufacturing processes face challenges with untimely and inaccurate data acquisition, lack of effective synchronization between assembly sites and information systems, and inadequate data analysis, leading to dispersed quality data and difficulty in improving production quality.
Innovation Solution
An assembly optimization method based on Industrial Internet of Things (IIoT) that involves obtaining session assembly data through an IIoT perceptual control platform, uploading it to an IIoT management platform, determining an optimized assembly scheme, generating regulation instructions, and adjusting device operation parameters to improve production efficiency and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection or simple device monitoring is used in traditional manufacturing processes, then data acquisition is simpler and requires less complex infrastructure, but data acquisition becomes untimely and inaccurate, and quality data becomes dispersed and difficult to analyze
Solution Approach 1:
The patent implements real-time feedback mechanisms where assembly data is continuously collected from the production line, analyzed by the optimization system, and used to dynamically adjust assembly parameters. The system monitors assembly quality metrics and feeds this information back to automatically adjust assembly schemes, ensuring timely and accurate data acquisition while maintaining closed-loop control for continuous improvement
Solution Approach 2:
The patent introduces an IIoT-based assembly optimization system as an intermediary layer between the physical assembly line and the information system. This intermediary collects data from sensors on the production line, processes it through optimization algorithms, and translates it into actionable assembly schemes, thereby bridging the gap between manual monitoring and intelligent control without requiring complete system redesign
2Reliability
If traditional data collection methods are used, then the system is easier to implement, but there is lack of effective synchronization mechanisms between assembly sites and information systems
Solution Approach 1:
The patent implements a universal IIoT platform that serves multiple functions: data collection from assembly lines, real-time synchronization with information systems, optimization calculation, and control instruction transmission. This multi-functional system ensures reliable synchronization across different sites while consolidating complexity into a single integrated platform rather than requiring separate systems for each function
Solution Approach 2:
The IIoT-based optimization system acts as an intermediary that establishes effective synchronization mechanisms between assembly sites and information systems. It collects assembly data in real-time, synchronizes this information with the central information system, and maintains consistent data flow across distributed locations, thereby ensuring reliability without requiring direct complex connections between all components
3Loss of information
If fragmented and unsystematic data is used, then data collection is simpler, but it becomes difficult to analyze and improve quality problems
Solution Approach 1:
The patent merges fragmented assembly data from various sources and departments into a unified data structure through the IIoT platform. It consolidates quality metrics, assembly parameters, device status, and process information into a comprehensive dataset that can be systematically analyzed, thereby preventing information loss while managing complexity through centralized data integration rather than distributed data silos
Solution Approach 2:
The patent segments the data management process into distinct functional modules: data collection from assembly lines, data transmission through IIoT networks, data storage in standardized formats, data analysis through optimization algorithms, and action generation. This segmentation allows each module to handle specific aspects of data management independently, reducing overall complexity while ensuring complete and systematic data processing
4Productivity
If real-time monitoring and dynamic adjustment of assembly schemes is implemented, then production efficiency and product quality are significantly improved, but the system complexity and implementation difficulty increase
Solution Approach 1:
The patent implements real-time feedback loops where assembly performance data is continuously monitored and fed back to the optimization system. This enables dynamic adjustment of assembly schemes during production, improving efficiency through automated real-time optimization rather than static pre-planned procedures, while the feedback mechanism handles the complexity of continuous monitoring and adjustment
Solution Approach 2:
The patent enables the assembly system to self-optimize by automatically analyzing assembly data, generating optimized assembly schemes, and implementing adjustments without requiring constant human intervention. The system serves itself by autonomously improving assembly processes based on real-time data, thereby increasing productivity while reducing the operational complexity burden on human operators
Data Source
AI summary
Disclosed is an assembly optimization method, system, and medium based on Industrial Internet of Things (IIoT). The assembly optimization method comprises: obtaining and uploading session assembly data of a production line; obtaining an initial assembly scheme; determining an optimized assembly scheme; determining a target assembly scheme; generating and storing an assembly regulation instruction; in response to an adjustment time being reached, sending an assembly regulation instruction to regulate a device operation parameter of a production device and a conveyor belt parameter of a conveyor belt; obtaining reference assembly data of the production line; in response to the reference assembly data meeting a correction condition: determining a correction optimization session of a current assembly scheme; generating a corrected assembly scheme; storing the corrected assembly scheme and generating a correction regulation instruction; and sending the correction regulation instruction to correct the device operation parameter and the conveyor belt parameter.