Digital warehousing and logistics management system based on AI intelligent scheduling technology

By applying AI intelligent scheduling technology in digital warehousing, building theoretical energy consumption and efficiency prediction models, optimizing material location, and monitoring logistics status in real time, the problem of unreasonable material location layout in digital warehousing is solved, and logistics efficiency and energy consumption utilization are improved.

CN118966976BActive Publication Date: 2025-03-25GUANGDONG DEEP BLUE INTELLIGENT SOFTWARE CO LTD +1
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Patent Information

Application Number
CN202411437479.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-15
Publication Date
2025-03-25
Estimated Expiration
2044-10-15

AI Technical Summary

Technical Problem

In the prior art, the unreasonable layout of materials in digital warehousing is caused by low logistics efficiency, high energy consumption, and the lack of effective supervision and early warning mechanisms.

Method used

Using a digital warehousing and logistics management system based on AI intelligent scheduling technology, through warehousing and logistics data collection, status analysis, location analysis and optimization modules, a theoretical energy consumption prediction model and theoretical efficiency prediction model are built, material position layout is optimized, and logistics efficiency, energy consumption and material position adaptability are monitored in real time, early warnings are issued and measures are taken in a timely manner.

Benefits of technology

It improves the efficiency and energy consumption utilization rate of warehousing logistics, avoids resource waste caused by unreasonable material location, and ensures the smooth operation of warehousing logistics.

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Abstract

The present invention discloses a digital warehousing and logistics management system based on AI intelligent scheduling technology, and specifically relates to the technical field of digital warehousing management, including: collecting material information, actual logistics efficiency and actual material energy consumption in digital warehousing; building a theoretical energy consumption prediction model and a theoretical efficiency prediction model based on machine learning, inputting logistics execution instructions into the trained theoretical energy consumption prediction model and theoretical efficiency prediction model, and outputting theoretical efficiency and theoretical energy consumption; constructing a material position fitness function of the material according to the theoretical efficiency and theoretical energy consumption of the material, taking the highest material position fitness as the goal, outputting the updated position coordinates of each material type through an optimization algorithm, and obtaining the maximum material position fitness when all materials are in the updated position coordinates; optimizing the material position based on the updated position coordinates, and solving the problem of resource waste caused by unreasonable material stacking position in the prior art.
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