Logistics order distribution volume prediction method and device based on multi-level network point architecture

By employing sliding window anomaly detection, multi-level network hierarchy construction, multi-dimensional weight ranking, and user behavior learning optimization, combined with an integrated prediction model, the data quality and interactive experience issues in logistics delivery volume prediction were resolved, achieving efficient prediction and management.

CN122367320APending Publication Date: 2026-07-10上海乾臻信息科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
上海乾臻信息科技有限公司
Filing Date
2026-05-09
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Current logistics delivery volume forecasting suffers from several problems, including a lack of standardized data collection processes, untimely data updates, chaotic multi-level network hierarchy, a lack of scientific basis for network ranking, a forecasting model that does not integrate multi-dimensional influencing factors, and a poor user experience.

Method used

A sliding window anomaly detection model is used to identify and correct abnormal data, a multi-level network hierarchy is constructed, a multi-dimensional weight ranking model is introduced, user behavior learning is combined to optimize the display, an integrated prediction model is used to integrate multiple analysis methods, and a prediction error feedback mechanism is established.

Benefits of technology

It provides a high-quality data foundation, improves the convenience of branch management and the accuracy of predictions, enhances the interactive experience, reduces operating costs and improves service quality.

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Abstract

This invention relates to the field of parcel delivery volume prediction technology, and discloses a method for predicting logistics parcel delivery volume based on a multi-level network architecture. The method includes: acquiring raw data and standardizing the data; constructing a multi-level network hierarchy and a multi-dimensional weighted ranking model; displaying the data in multiple dimensions; calculating the confidence interval based on the prediction model and attaching a confidence level label to each predicted value; introducing a user behavior learning model to record user click habits and query preferences, and optimizing the network display order and recommendation logic; using an integrated prediction model to fuse time series analysis, regression analysis, and machine learning prediction methods to form a combined prediction result; establishing a prediction error feedback mechanism to compare the actual delivery volume with the predicted delivery volume and optimize model parameters; and adopting a hierarchical prediction strategy. This invention provides reliable support for logistics delivery planning and resource allocation, reduces operating costs, and improves service quality.
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