A method, system, device and medium for scheduling a pilot station vehicle fleet pick-up and drop-off task

By using improved clustering and dynamic programming algorithms, the task connection of pilot station fleets is identified and optimized in real time, generating efficient carpooling and low-empty-run scheduling schemes. This solves the scheduling problem under manual dependence and realizes efficient and low-cost operation of fleet scheduling.

CN122243144APending Publication Date: 2026-06-19NEZHA SMART TECHNOLOGY (SHANGHAI) CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NEZHA SMART TECHNOLOGY (SHANGHAI) CO LTD
Filing Date
2026-05-22
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

The dispatching of pilot station fleets relies on manual experience, resulting in poor task coordination and delayed information synchronization. This leads to low pick-up and drop-off efficiency, high operating costs, and difficulty in achieving efficient vehicle carpooling and minimizing empty mileage.

Method used

By employing improved clustering and dynamic programming algorithms, and through real-time synchronization of task information, the system identifies and prioritizes the construction of connecting road segments, generates a scheduling scheme that maximizes carpooling efficiency and minimizes empty mileage, and dynamically recalculates and updates the scheme when tasks change.

Benefits of technology

It has enabled a shift from passive recording to proactive decision-making, significantly improving the efficiency and robustness of fleet scheduling, reducing operating costs, and maintaining the optimization and consistency of scheduling schemes, especially in environments where pilotage missions change frequently.

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Abstract

This application discloses a method, system, equipment, and medium for scheduling pilot station fleet pick-up and drop-off tasks, applied in the field of pilotage information technology. The scheduling method includes: online input of pick-up and drop-off task information; estimation of task travel time, assigning lower toll cost weights to connecting routes than regular routes; using dual association criteria in clustering to prioritize task combinations with connecting relationships into the same task cluster; generating a dispatch plan based on the estimation and clustering results, planning a coherent operation sequence based on the current task destination and the starting points of historical pending tasks, and triggering recalculation and synchronously updating the dispatch plan when tasks change. This application transforms the scheduling process from a passive recording mode of isolated task processing to an active decision-making mode that prioritizes connections and ensures end-to-end connectivity by incorporating the "destination-start point coherence" constraint throughout the travel estimation and clustering stages. This maximizes carpooling efficiency and minimizes empty mileage, significantly reducing operating costs.
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