Logistics network operation data management method, apparatus and device, and storage medium

Through the cleaning, standardized processing and construction of abnormal detection models of logistics outlet operation data, operation abnormalities are discovered in a timely manner, and the problems of incomplete collection of operation data and single analysis methods in the existing technology are solved, and the operation management efficiency and service quality of logistics outlets are improved.

CN119692888APending Publication Date: 2025-03-25上海乾臻信息科技有限公司
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Patent Information

Application Number
CN202411728208.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-28
Publication Date
2025-03-25

AI Technical Summary

Technical Problem

The existing technology cannot comprehensively and accurately collect operation data of logistics outlets, analyze operation data from multiple aspects, and promptly discover abnormal situations in operation data, resulting in low management efficiency, difficult service quality, and difficult cost control.

Method used

By obtaining logistics outlet operation data, cleaning and supplementing and standardizing processing, building an operational abnormality detection model, using feature data sets to train the model, output operational abnormality detection results, and establishing an operational decision support model to feedback the decision plan for the abnormality detection results.

Benefits of technology

It realizes comprehensive and accurate collection and analysis of logistics outlet operation data, timely discovers operational abnormalities, improves operation management efficiency, and ensures service quality and cost control.

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Abstract

The invention discloses a logistics network operation data management method, and the method comprises the steps: obtaining logistics network operation data, which is used for obtaining a management factor set affecting the logistics network operation data and the logistics network operation data; cleaning, supplementing and standardizing the logistics network operation data to obtain standardized operation data; building an operation anomaly detection model, training the network operation anomaly detection model by using the feature data set, and determining the trained network operation anomaly detection model; inputting the standardized operation data of the logistics network into a network operation anomaly detection model to output a network operation anomaly detection result; and establishing an operation decision support model for feeding back a decision scheme for the network anomaly detection result. According to the method, the factors influencing operation management can be determined, the operation state of the logistics network can be effectively and accurately detected through the operation data, and meanwhile, the logistics network with the poor operation state can be effectively supervised and optimized.
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