Machine Vision Tracking for Real-Time Factory Progress Data
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Solution Overview
Problem
Conventional systems for tracking and entering data in factories or enterprise resource planning platforms lack automation, leading to errors and incomplete data, failing to provide real-time insights into productivity.
Innovation Solution
A learning system that utilizes machine vision and neural networks to automate the identification and analysis of data streams, incorporating user feedback for improved object detection and causal relationship understanding, enabling dynamic learning and self-correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data entry methods are used in conventional systems, then system complexity is reduced, but data accuracy and completeness deteriorate
Solution Approach 1:
The patent replaces manual mechanical data entry processes with an automated machine vision system that uses cameras and image processing algorithms to automatically detect, track, and record worker movements, tasks performed, and time spent. This substitution eliminates human error in data entry while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system enables self-service automation where the machine vision system autonomously captures data without requiring manual intervention. The automated tracking system independently monitors work processes, generates reports, and updates databases without human involvement in data collection, thereby improving accuracy while the system manages its own complexity through integrated design.
2Reliability
If automated machine vision systems are implemented, then data accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides the automated tracking system into separate functional modules: image capture modules, object detection modules, tracking modules, data processing modules, and reporting modules. This segmentation allows each component to be optimized independently, improving reliability while managing overall system complexity through modular architecture that facilitates maintenance and debugging.
Solution Approach 2:
The machine vision system is designed with multi-functionality to perform multiple tasks including worker identification, task monitoring, time tracking, and productivity analysis using the same hardware infrastructure. This universal approach improves data reliability across multiple measurement dimensions while avoiding the complexity of separate specialized systems for each function.
3Productivity
If manual data tracking is used, then ease of operation is maintained, but productivity of data collection deteriorates
Solution Approach 1:
The machine vision system operates continuously and automatically to track work processes without interruption, capturing data in real-time as workers perform tasks. This continuous automated monitoring dramatically increases data collection productivity compared to periodic manual checks, while the system maintains ease of operation through automated data processing and reporting functions that require minimal user interaction.
Solution Approach 2:
The patent introduces an automated intermediary system between the work process and data storage that automatically bridges the gap between physical worker actions and digital data records. This intermediary machine vision system continuously translates physical activities into structured data without requiring manual intervention, thereby increasing productivity while simplifying operation through automated mediation.
4Loss of time
If real-time automated tracking is implemented, then data up-to-date availability improves, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring machine vision cameras to continuously capture images and pre-programming detection algorithms to immediately process incoming visual data. This preliminary setup enables real-time tracking with minimal processing delay, improving data availability speed while the pre-configured automated systems manage complexity through standardized protocols and pre-established data processing pipelines.
Data Source
AI summary
A method for providing access to automated tracking system data, includes processing, by a machine vision component in communication with a learning system, a video file to detect at least one object in the video file. The machine vision component generates an output including data relating to the at least one object and the video file. A learning system analyzes the output and identifies an attribute of the video file. The method includes analyzing, by a state machine in communication with the learning system, the output and the attribute and the video file. The method includes determining, by the state machine, a level of progress made towards a goal through utilization of the at least one object. The method includes modifying, by the learning system, a user interface to display an indication of the determination by the state machine.