Scheduling duration adjusting method and device based on gain model, product and medium

By constructing an order relationship graph and using a graph neural network to predict gain values, the scheduling time of orders is dynamically adjusted, solving the problem of inaccurate delivery time prediction in the instant delivery system and improving scheduling efficiency and resource utilization.

CN121745601APending Publication Date: 2026-03-27RAJAX NETWORK &TECHNOLOGY (SHANGHAI) CO LTD
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Patent Information

Application Number
CN202511955485.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-22
Publication Date
2026-03-27

AI Technical Summary

Technical Problem

In existing on-demand delivery systems, inaccurate delivery time predictions lead to unreasonable scheduling time settings, which may result in missed order consolidation opportunities or wasted resources.

Method used

A gain-based scheduling duration adjustment method is adopted. By constructing an order relationship graph and extracting graph embedding vectors using a graph neural network, the gain value under the duration adjustment strategy is predicted, and the initial scheduling duration of orders is dynamically adjusted.

Benefits of technology

Accurately identify which order execution time adjustment strategies can improve the probability of collaborative delivery, dynamically adjust the scheduling time, prevent unreasonable initial scheduling time settings, and improve delivery efficiency.

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Abstract

The invention provides a scheduling duration adjustment method and device based on a gain model, a product and a medium. The method comprises the steps of obtaining a to-be-distributed delivery order set with an association relationship; constructing an order relation graph of the order set; inputting the order relation graph into a gain model, so that the gain model extracts a graph embedding vector of the order relation graph based on a graph neural network, and predicts a gain value corresponding to each order under one or more predefined duration adjustment strategies for each order based on the graph embedding vector; the duration adjustment strategy comprises a strategy for performing duration adjustment on the initial scheduling duration; the gain value characterizes the probability gain that the order and other orders in the order set are distributed to the same delivery capacity relative to the non-execution of the order execution duration adjustment strategy; and determining a final adjustment strategy of the initial scheduling duration of the order according to the gain value of the order.
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