Construction site people and vehicle management system for project management

By introducing a multi-module collaborative real-time management system to the construction site, and combining UWB positioning and multimodal reinforcement learning obstacle avoidance, the risks of mixed pedestrian and vehicle traffic and inefficient resource scheduling in traditional construction site management have been solved, achieving efficient and safe construction management.

CN120764919BActive Publication Date: 2026-06-05CHINA CONSTR SECOND ENG BUREAU LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA CONSTR SECOND ENG BUREAU LTD
Filing Date
2025-06-25
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Traditional construction site management relies on manual inspections and static rules, which leads to problems such as high risk of mixed pedestrian and vehicle traffic, inefficient resource scheduling, lax access control, and lagging data analysis. It also lacks real-time collision warnings and optimization decision support.

Method used

It employs modules for personnel positioning and trajectory tracking, vehicle scheduling and path planning, dynamic obstacle avoidance and collision warning, access control and identity verification, real-time data analysis and visualization, and automatic response to abnormal events. Combined with UWB positioning, multimodal reinforcement learning obstacle avoidance algorithms, and hypergraph neural network access control, it achieves real-time monitoring, dynamic planning, and automatic response.

Benefits of technology

It improved the safety and efficiency of construction sites, reduced the collision rate and equipment idle rate, optimized resource allocation, and enabled real-time decision support and high-reliability management.

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Abstract

The application provides a construction site people and vehicle management system for project management, comprising a personnel positioning and trajectory tracking module, a vehicle scheduling and path planning module, a dynamic obstacle avoidance and collision warning module, a permission management and identity verification module, a real-time data analysis and visualization module, and an abnormal event automatic response module; the application improves safety, optimizes efficiency, saves costs, and improves the intelligence of decision-making. The combination of LSTM trajectory prediction and dynamic genetic algorithm realizes a "prediction-planning-response" closed loop, and the introduction of edge computing-driven real-time permission hierarchical control in construction management ensures that the system can achieve millimeter-level obstacle avoidance accuracy and millisecond-level permission response in complex construction site environments, ensuring strict real-time performance and high reliability of multiple modules.
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