Cloud store long-cloud on-duty rider take-out goods taking management system

Through the cloud store manager cloud guard rider takeaway pickup management system, the AI ​​intelligent prediction and scheduling module is used to analyze multi-dimensional information, predict peak hours in advance and automatically adjust resource allocation, the problem that traditional takeaway management systems cannot respond to changes in demand in a timely manner, and efficient order processing and improved service quality are achieved.

CN120124972APending Publication Date: 2025-06-10GUANGDONG ZHEXIAN INTELLIGENT TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202510277966.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-10
Publication Date
2025-06-10

AI Technical Summary

Technical Problem

Traditional takeaway management systems lack the ability to analyze real-time and multi-dimensional data, resulting in the inability to respond to changes in demand in a timely manner, resulting in delays in order processing and customer dissatisfaction.

Method used

The cloud store manager cloud guard rider takeaway pickup management system is adopted. The system includes remote servers, smart terminal devices, aggregate payment interfaces, intelligent LED light prompt modules, customer service modules, sales data interconnection system, AI intelligent prediction and scheduling modules, etc. It analyzes historical order data, weather conditions, holiday effects and other information through machine learning algorithms, predicts peak hours in advance and automatically adjusts resource allocation.

Benefits of technology

The system can predict peak hours in advance, automatically adjust resource allocation, avoid order delays, improve service quality, enhance customer trust and loyalty, and improve operational efficiency and economic benefits.

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Abstract

The invention, which belongs to the technical field of cloud e-commerce, discloses a cloud store long-cloud-on-duty rider take-out goods taking management system comprising at least one remote server for providing uninterrupted operation support all the year round; an intelligent terminal device; a plurality of aggregate payment interfaces; the intelligent LED colored lamp prompt module is used for accurately indicating the positions of commodities needing to be picked up by a take-out rider in combination with order information and assisting in completing the self-service commodity picking process; a customer service module; a sales data interconnection system; and the AI intelligent prediction scheduling module analyzes historical order data, weather conditions and holiday effect multi-dimensional information by using a machine learning algorithm, predicts a peak period in advance, and automatically adjusts in-store resource allocation. According to the invention, the peak period can be predicted in advance, and in-store resource allocation can be automatically adjusted. According to the prediction and adjustment mechanism, the shop can make preparation before the peak period of demand, and delay or disorder caused by temporary resource allocation is avoided.
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