Handling Control Strategy Using KPI Feedback for Piece Goods
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Solution Overview
Problem
Existing handling devices for piece goods struggle to adapt their control strategies to changing boundary conditions, such as variations in piece goods, leading to subjective and time-consuming manual adjustments.
Innovation Solution
A computer-implemented method that acquires piece good information, transmits it to a data center, determines key performance indicators, and adapts the control strategy based on these indicators, enabling automatic reaction to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual control adjustment is used to adapt handling devices to changing piece goods, then the control strategy can be fine-tuned based on user observations, but the process becomes time-consuming and subjective
Solution Approach 1:
The handling device automatically adjusts its own control strategy by processing piece good information through trained machine learning models, eliminating the need for manual intervention. The system serves itself by autonomously adapting to changing boundary conditions based on real-time data analysis.
Solution Approach 2:
The system continuously collects piece good information from sensors and cameras, processes this data through machine learning models, and uses the results to automatically adjust control parameters. This closed-loop feedback mechanism enables automatic adaptation without manual intervention.
2Adaptability or versatility
If manual control adjustment is used to adapt handling devices to changing piece goods, then the control strategy can be fine-tuned based on user observations, but the process depends on user experience and cannot account for all boundary conditions
Solution Approach 1:
The patent replaces manual mechanical adjustment with an automated information processing system. Machine learning models process piece good data objectively, substituting human judgment with algorithmic decision-making that consistently evaluates all boundary conditions without subjective bias.
Solution Approach 2:
The machine learning model acts as an intermediary between piece good characteristics and control strategy adjustment. This intermediary objectively processes all input data including images, dimensions, and physical properties, translating them into optimal control parameters without human intervention.
3Extent of automation
If automated control strategy determination is implemented using piece good information and key performance indicators, then the system can automatically adapt to changing boundary conditions, but the system complexity increases
Solution Approach 1:
The patent employs universal machine learning models that can process multiple types of piece good information (images, dimensions, physical properties) and determine various control parameters (speed, acceleration, force) using the same underlying system architecture. This multi-functionality reduces overall system complexity despite the comprehensive automation achieved.
Data Source
AI summary
A computer-implemented method for determining a control strategy for controlling a handling process of at least one handling device for handling piece goods includes detecting or obtaining at least one piece good information of at least one piece good which has been handled by the handling device, transmitting the at least one piece good information to a data center, determining at least one key performance indicator of the handling process of the handling device based on the at least one piece good information, determining a control strategy for the at least one handling device based on the at least one key performance indicator, a related computer program and a related handling device.