A parking resource intelligent scheduling system based on multi-agent cooperation

By using a multi-agent collaborative architecture, the parking system achieves autonomous decision-making and cross-scenario sharing, solving the problems of insufficient autonomous decision-making capabilities, difficulty in multi-agent collaboration, and poor user experience in existing parking systems. This improves the efficiency of parking resource utilization and user experience, and promotes green travel.

CN122414618APending Publication Date: 2026-07-17SHANGHAI LINGANG JINGHONG SECURITY TECH DEV CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI LINGANG JINGHONG SECURITY TECH DEV CO LTD
Filing Date
2026-03-31
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing parking systems lack autonomous decision-making capabilities, have low dynamic response efficiency, are difficult to coordinate among multiple stakeholders, and provide poor user service experience, failing to meet the intelligent and efficient needs of urban parking management across all scenarios.

Method used

It adopts a multi-agent collaborative architecture, including an environmental perception agent, an equipment control agent, a decision-making center agent, a user interaction agent, and a property management agent. Through autonomous decision-making, real-time perception, dynamic pricing, and personalized services, it realizes intelligent scheduling and cross-scenario sharing of parking resources.

Benefits of technology

It has improved the efficiency of parking resource utilization, optimized the user parking experience, reduced property operation costs, promoted green and low-carbon travel, and solved the urban parking problem and traffic congestion problem.

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Abstract

The application relates to the cross technical field of artificial intelligence and intelligent traffic, and provides a parking resource intelligent scheduling system based on multi-agent cooperation. An environment perception agent and a device control agent are arranged in an edge layer; the environment perception agent collects parking space and vehicle related data and generates a parking space occupation heat map; the device control agent remotely controls various hardware devices of a parking lot; a decision hub agent, a user interaction agent and a property management agent are arranged in a cloud layer; the decision hub agent is used for parking space demand prediction, dynamic pricing and path planning; the user interaction agent is used for providing personalized services and non-conscious payment and other interactive functions for users; the property management agent is used for processing user complaints and realizing visual management of parking lot resource scheduling; a multi-agent cooperation mechanism is used for realizing low-delay and high-reliability communication between the agents in the edge layer and the cloud layer. The application solves the problems of traditional parking system information islands, scheduling lag and poor user experience.
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