Industrial IoT Parameter Control for Multi-Device Manufacturing
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Solution Overview
Problem
In manufacturing, determining optimal working parameters for multiple devices is time-consuming and labor-intensive, leading to inaccuracies and increased costs, as each parameter must be manually or computationally calculated and tested individually, with interactions between parameters further complicating the process and affecting product quality.
Innovation Solution
An Industrial Internet of Things (IoT) system with a five-platform structure, comprising user, service, management, sensor network, and object platforms, processes and combines data to determine optimal working parameter combinations efficiently, reducing manual intervention and enabling real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimal working parameters are determined through manual calculation and continuous simulation experiments, then the optimal parameter values can be found, but the process is extremely time-consuming and labor-intensive
Solution Approach 1:
The patent creates a digital twin (virtual model) of the manufacturing system that replicates the physical system's behavior. This virtual model allows parameter optimization to be performed through simulation without requiring continuous physical experiments, thereby reducing time and labor while maintaining optimization accuracy.
Solution Approach 2:
The system pre-establishes the digital twin model and parameter relationships before actual manufacturing. By having the virtual model ready in advance, parameter optimization can be quickly performed through simulation rather than requiring time-consuming trial-and-error experiments during production.
2Measurement precision
If optimal parameters are determined individually for multiple working parameters and then combined, then each parameter can be optimized, but the combined parameters cause uncertainty and inaccuracy
Solution Approach 1:
The patent merges multiple individual parameter optimizations into a unified digital twin model that considers parameter interactions simultaneously. The virtual model integrates all working parameters and their relationships, allowing for comprehensive optimization that accounts for parameter couplings rather than treating them separately.
Solution Approach 2:
The digital twin model incorporates feedback mechanisms that continuously evaluate the combined effect of multiple parameters. By simulating parameter interactions in the virtual model, the system can identify and adjust for uncertainties arising from parameter combinations, thereby improving the reliability of optimized parameter sets.
3Reliability
If working parameters are limited to certain intervals without further restriction, then devices can operate within safe ranges, but optimal parameters (certain value or small interval) cannot be achieved
Solution Approach 1:
The patent applies local quality by identifying specific sub-intervals within the general safe operating ranges where optimal parameters exist. The digital twin model analyzes the parameter space to pinpoint precise optimal values or narrow intervals for each working parameter, allowing the system to operate at optimal points while remaining within safe boundaries.
Solution Approach 2:
The system dynamically adjusts working parameters within safe intervals to reach optimal values. The digital twin enables real-time simulation and evaluation, allowing the system to navigate within the safe operating range and converge to optimal parameter values that maximize manufacturing quality while maintaining safety constraints.
4Adaptability or versatility
If multiple different parameters involving device interactions are considered, then comprehensive control is achieved, but the complexity of determining optimal parameters increases significantly
Solution Approach 1:
The digital twin serves as an intermediary between the complex physical system with multiple interacting parameters and the optimization process. It virtualizes the parameter interactions, allowing complex relationships to be simulated and analyzed without directly manipulating the physical system, thereby reducing determination complexity while maintaining comprehensive control.
Solution Approach 2:
The patent segments the complex parameter optimization problem into manageable components within the digital twin model. By dividing the system into modular virtual representations of different devices and parameters, the system can analyze interactions systematically rather than dealing with the full complexity at once, reducing the overall determination complexity.
Data Source
AI summary
The present disclosure discloses an Industrial Internet of Things, a control method, and a medium for regulating multi-type of working parameters. The Industrial Internet of Things may include a user platform, a service platform, a management platform, a sensor network platform and an object platform that interact in turn. The service platform, the management platform and the sensor network platform may be all arranged in a front sub-platform layout. The control method may be applied to the Industrial Internet of Things. The optimal value of the working parameter may be obtained based on the product manufacturing data, and the optimal selection of the parameter may be realized under the condition of ensuring the production, and then the working parameters of all the manufacturing devices may be further regulated or determined.