IoT Logistics Routing With Goods Classification and Driver Scoring
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Solution Overview
Problem
The logistics industry faces challenges in ensuring the integrity and timely delivery of goods, particularly with the increasing demand for specialized transportation of items like dangerous goods, perishable refrigerated goods, valuable goods, vivid plants, vivid animals, and large bulky items, where existing methods lack efficient classification and driver selection processes.
Innovation Solution
A logistics management system utilizing IoT-based methods for classifying goods using a pre-trained classification model, matching appropriate transport vehicles, and selecting drivers based on historical performance scores to ensure safe and timely delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual classification and driver selection methods are used, then operational simplicity is maintained, but logistics efficiency and service quality deteriorate
Solution Approach 1:
The system enables automated self-service through the goods classification model that automatically categorizes goods based on packaging images, and the driver selection mechanism that autonomously identifies suitable drivers based on historical trajectory data, eliminating manual classification and driver assignment
Solution Approach 2:
The patent replaces manual mechanical operations with automated systems: optical character recognition and image processing algorithms substitute for manual goods classification, while digital trajectory analysis and scoring systems replace manual driver selection processes
2Productivity
If automated classification and driver selection systems are implemented, then logistics efficiency is improved, but system complexity increases
Solution Approach 1:
The system uses packaging images as copies or representations of the actual goods to determine classification, avoiding the need for physical inspection while accurately identifying goods types through image analysis by the pre-trained classification model
Solution Approach 2:
The goods classification model is pre-trained on extensive datasets before deployment, performing preliminary learning and pattern recognition work in advance, so that during actual operation it can quickly and accurately classify goods without requiring complex real-time processing
3Reliability
If driver selection is based on comprehensive historical data analysis, then service quality is improved, but processing time increases
Solution Approach 1:
The system continuously collects feedback from historical transportation processes including trajectory data, delivery outcomes, and performance metrics, using this feedback to refine driver scoring and selection, thereby improving delivery reliability through learned patterns from past performance
Solution Approach 2:
The patent transforms complex qualitative driver assessment parameters into quantifiable numerical scores based on historical trajectory and performance data, enabling efficient comparison and selection while maintaining comprehensive evaluation of driver reliability
Data Source
AI summary
A logistics management method based on the Internet of Things includes: acquiring a packaging image of currently transported goods, information of a departure point of the currently transported goods, and information of an arrival point of the currently transported goods; classifying the currently transported goods to obtain a category of the currently transported goods; matching corresponding first transport vehicles according to the category of the currently transported goods and determining a driver pool corresponding to each of the first transport vehicles; and calculating a final transportation score corresponding to each driver based on historical transportation trajectories of each driver in the driver pool and a historical transportation score of each driver in each historical transportation process, and selecting a driver of the highest final transportation score to transport the currently transported goods.