Centralized IIoT Platform for Multi-Process Abnormality Control
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Solution Overview
Problem
Existing Industrial Internet of Things (IIoT) technologies for intelligent manufacturing are limited to managing and controlling a single production process, lacking universality and timely countermeasures for abnormal production events, which hinders their widespread application.
Innovation Solution
An intelligent manufacturing method and system based on a centralized service platform, integrating a user platform, service platform, management platform, sensor network platform, and object platform, enabling unified data collection, processing, and control across multiple processes, with real-time monitoring and abnormality detection to trigger corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing IIoT technology is used for intelligent manufacturing, then a single production process can be managed and controlled, but it lacks universality and cannot be applied to multiple different processes
Solution Approach 1:
The patent applies universality by designing a standardized data interaction framework that can be applied across multiple different manufacturing processes. The service platform defines universal data models, interaction protocols, and service interfaces that work across diverse processes such as machining, assembly, and inspection, allowing the system to serve multiple functions without requiring process-specific customizations.
Solution Approach 2:
The patent segments the IIoT system into distinct functional layers: service platform, management platform, and edge devices. Each layer has specific responsibilities and standardized interfaces, allowing the system to be configured for different processes by assembling appropriate components rather than redesigning the entire system, thus improving versatility while managing complexity.
2Reliability
If existing IIoT technology is used for intelligent manufacturing, then real-time data collection and processing can be achieved, but timely countermeasures cannot be taken in the event of abnormal production
Solution Approach 1:
The patent implements feedback mechanisms where the service platform continuously monitors process data, compares it against predefined thresholds and models, and automatically triggers alerts or corrective actions when abnormalities are detected. This closed-loop feedback system enables timely response to production issues without requiring complex manual monitoring systems.
Solution Approach 2:
The patent applies preliminary action by pre-configuring abnormality detection rules, thresholds, and response protocols before production begins. The service platform is pre-loaded with process models and anomaly detection algorithms that automatically activate when deviations occur, enabling rapid response without requiring complex real-time decision-making systems.
3Adaptability or versatility
If a centralized service platform is implemented to manage multiple processes, then comprehensive control and timely intervention can be achieved, but system complexity increases
Solution Approach 1:
The patent resolves complexity by organizing the centralized platform in a hierarchical dimension rather than a flat structure. The service platform sits at the top providing unified management, while management platforms and edge devices operate at lower levels with standardized interfaces. This dimensional organization allows multi-process management capability while keeping each layer's complexity manageable through clear separation of concerns.
Data Source
AI summary
The present disclosure provides an intelligent manufacturing method based on Industrial Internet of Things with a centralized service platform, including: the user platform inputting the parameter configuration information, and the service platform disassembling it into multiple sets of configuration data groups according to process; the management platform storing and processing the configuration data groups, and the configuration data group being used as a reference data group; the object platform taking the perception information as a verification data group at a set time interval; the management platform receiving and processing the verification data group, and sending the reference data group and the verification data group to the service platform; the service platform receiving and comparing the reference data group and the verification data group; if comparisons exceed the set threshold range, generating stop operation instructions and sending them to the object platform to control the production line device to stop running.