Influenza vaccine transfer optimization method and system based on data driving

By using a machine learning prediction model driven by multi-source dynamic data and online calibration optimization, the problem of characterizing uncertainties in influenza vaccine transportation was solved, enabling precise characterization and risk control of the influenza vaccine transportation process, and improving the reliability and adaptability of the transportation plan.

CN122335154APending Publication Date: 2026-07-03DONGGUAN UNIV OF TECH
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Patent Information

Application Number
CN202610471119.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-10
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing influenza vaccine transport management plans are unable to effectively reflect uncertainties in the real environment, such as traffic congestion, weather changes, loading and unloading queues, and refrigerant decay, resulting in large discrepancies between plans and reality.

Method used

A machine learning prediction model driven by multi-source dynamic data is used to predict and optimize transportation time, node waiting time and temperature control risks. Combined with cold chain and capacity constraints, a transshipment optimization model is constructed, and online calibration and re-optimization are performed during the execution process.

Benefits of technology

It enables precise mapping of the influenza vaccine transport process, reduces the risk of exceeding temperature limits, improves the reliability and robustness of the transport plan, and ensures cold chain safety and timeliness.

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Abstract

The invention provides an influenza vaccine transfer optimization method and system based on data driving, and relates to the technical field of data processing and intelligent optimization, and the method comprises the steps: obtaining multi-source dynamic data corresponding to an influenza vaccine transfer task, and carrying out the time alignment and feature construction of the multi-source dynamic data, according to the influenza vaccine transfer optimization method and system based on data driving, feature vectors used for representing a candidate transfer link set and a candidate transfer node set are obtained, and candidate transfer links are used for representing a transport mode and path combination from a starting node to a destination node. Time alignment and feature construction are performed on multi-source dynamic data such as road traffic, weather, transfer node operation capability and cold chain state, a data-driven machine learning prediction model is established, and joint prediction is performed on transportation duration, node waiting time and temperature overrun risk. And the timeliness and the cold chain risk of the influenza vaccine transfer process are accurately depicted.
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