Machine-Learned Robotic Inspection for Logistics Goods Screening
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Solution Overview
Problem
Organizations face challenges in managing complex and voluminous data from smart devices and wearable technologies, leading to overwhelmed users and missed opportunities for insights, necessitating methods and systems to convert data into actionable insights and timely decisions.
Innovation Solution
A cloud-based management platform with a micro-services architecture, incorporating interfaces, network connectivity, adaptive intelligence, data storage, and monitoring facilities, along with robotic process automation, to manage value chain network entities from origin to customer use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection processes are used, then employees can review goods, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical inspection processes with an automated system comprising cameras, machine learning models, and robotic actuators. The machine learning model analyzes images captured by cameras to identify defective goods, eliminating the need for human employees to manually examine each item, thereby dramatically increasing inspection speed and reducing time loss.
Solution Approach 2:
The inspection system performs self-service by automatically capturing images of goods, analyzing them through machine learning algorithms, identifying defects, and triggering removal mechanisms without human intervention. The system autonomously completes the entire inspection workflow, from data acquisition to defect identification and corrective action initiation.
2Productivity
If automated inspection systems are implemented, then inspection speed increases, but system complexity increases
Solution Approach 1:
The automated inspection system is segmented into distinct functional modules: image capture devices (cameras), processing units (machine learning models), decision-making components (defect identification algorithms), and execution mechanisms (robotic actuators). This modular segmentation allows each component to be optimized independently while maintaining overall system productivity, managing complexity through functional decomposition.
3Measurement precision
If more employees are hired for inspection, then inspection thoroughness improves, but labor costs and time consumption increase
Solution Approach 1:
The patent replaces human employees with an automated machine learning-based inspection system that maintains high accuracy in identifying defective goods. The machine learning model is trained to recognize defect patterns with precision comparable to or exceeding human capability, while eliminating the time consumption associated with manual inspection processes.
Data Source
AI summary
A value chain system that provides recommendations for designing a logistics system generally includes a machine learning system that trains machine-learned models that output logistics design recommendations based on training data sets that each respectively defines one or more features of a respective logistic system and an outcome relating to the respective logistics system; an artificial intelligence system that receives a request for a logistics system design recommendation and determines the logistics system design recommendation based on one or more of the machine-learned models and the request; and a digital twin system that generates an environment digital twin of a logistics environment that incorporates the logistics system design recommendation, and one or more physical asset digital twins of physical assets. The digital twin system executes a simulation based on the logistics environment digital twin, the one or more physical asset digital twins.