An order information-based online car-hailing professional driver determination method

By using multi-dimensional data governance and multi-factor judgment models, the problems of single judgment dimensions and poor scenario adaptability in existing technologies have been solved, enabling accurate judgment and fine-grained analysis of the professionalism of ride-hailing drivers.

CN122390780APending Publication Date: 2026-07-14GUOJIAO INFORMATION (BEIJING) CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
GUOJIAO INFORMATION (BEIJING) CO LTD
Filing Date
2026-04-28
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

In existing technologies, the professionalization of ride-hailing drivers is determined solely by the number of orders, which leads to the omission of drivers with long-term and long-distance orders. Furthermore, the single fixed threshold lacks the ability to adapt to different scenarios and cannot meet the needs of refined operation and management.

Method used

By acquiring original ride-hailing operation data, conducting multi-dimensional data governance, extracting multi-dimensional operational behavior characteristics of drivers, and adopting a multi-factor judgment model that integrates order volume threshold, continuous operation duration threshold, and long-distance order proportion threshold, professional binary classification labels are generated. Then, association matching and aggregation analysis are performed to output the analysis results of professional drivers at the local level.

Benefits of technology

It enables precise labeling of drivers' professional attributes, improves the accuracy and flexibility of determining professional drivers, and meets the needs of refined operation and management.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a method for determining professional drivers of online car-hailing based on order information. The application carries out multi-dimensional data management on original operation data to obtain standardized order data; extracts multi-dimensional operation features such as daily order quantity, driving mileage, order duration and transaction amount based on driver identification, locality and date; adopts a multi-factor model integrating order quantity, continuous operation duration and long-distance order proportion threshold to determine professionalism; associates and matches the professionalism label with multi-dimensional features; and aggregates and analyzes the results in the dimension of locality and outputs the results. The application can more efficiently and accurately determine professional drivers.
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