IoT Machine Vision Production Line Parameter Adjustment
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Solution Overview
Problem
Existing industrial IoT systems rely heavily on sensor data for intelligent production line control, leading to increased system complexity and potential overfitting, which reduces control accuracy and increases development costs.
Innovation Solution
An IoT system that utilizes machine vision data to adjust production line parameters by processing image information from different process operations, sorting, and correcting image data to generate control parameters without increasing system complexity, using a service platform, management platform, and sensing network platforms to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If more sensors are used to obtain more accurate production line feedback, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces the traditional mechanical sensor-based detection system with a machine vision system using cameras and image processing algorithms. Instead of using multiple physical sensors to detect product characteristics, the system captures images and extracts features through computational methods, thereby achieving accurate measurement without increasing physical system complexity
Solution Approach 2:
The patent introduces image data as an intermediary between the product and the control system. Rather than directly measuring physical quantities with sensors, the system uses images as a medium to capture product information, which is then processed to extract meaningful features for production line feedback and control
2Measurement precision
If more sensors are deployed to improve control accuracy, then measurement precision is improved, but overfitting occurs which reduces control accuracy
Solution Approach 1:
The patent replaces sensor-based data collection with machine vision-based data collection. By using image processing and feature extraction algorithms, the system obtains robust product characteristics that are less prone to overfitting compared to raw sensor data, thereby improving the reliability and generalization of control models
Solution Approach 2:
The patent transforms physical product parameters into image domain parameters through photography and image processing. This parameter transformation changes the data representation from direct physical measurements to visual features, which provides better generalization capabilities and reduces overfitting in subsequent control applications
3Manufacturing precision
If machine vision data is used to adjust production line parameters, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex multi-sensor detection systems with a machine vision system. By using cameras and image processing to capture and analyze product characteristics, the system achieves precise production line parameter adjustment without the need for multiple physical sensors, thereby maintaining system simplicity while improving manufacturing precision
Solution Approach 2:
The patent makes the machine vision system universal by using it for multiple purposes: product detection, parameter measurement, quality inspection, and production line control. This multi-functional approach replaces what would otherwise require multiple specialized devices, achieving precise control without proportionally increasing system complexity
Data Source
AI summary
The present disclosure discloses an Internet of Things (IoT) system for industrial data processing, a control method, and a storage medium, and provides a technical solution for a timely adjustment of production line parameters according to image information generated during a machine vision data collection on the production line. Through a difference of the image information corresponding to different process operations, a processing situation of different process operations may be obtained, so that more accurate adjustment of the production line parameter may be achieved without increasing the system complexity, thereby effectively reducing a development cost of the IoT, and increasing accuracy of the intelligent manufacturing control.