Expressway intelligent monitoring and management system, method and electronic device

By constructing a closed-loop management system for intelligent monitoring and management of highways, and utilizing digital twins and spatiotemporal causal graphs, proactive identification and optimal intervention of traffic risks have been achieved. This solves the problem that existing systems cannot adapt to dynamic environments and improves prediction accuracy and decision-making effectiveness.

CN120998025BActive Publication Date: 2026-06-09JIANGSU JIAQING INFORMATION TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
JIANGSU JIAQING INFORMATION TECH CO LTD
Filing Date
2025-08-12
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing highway monitoring and management systems typically employ static predictive models that cannot adapt to changing traffic conditions along the route and lack the ability to deeply assess the consequences of intervention measures, resulting in a decline in predictive accuracy and control effectiveness over time.

Method used

Construct a closed-loop management and control system with dynamic self-correction capabilities, including a data acquisition module, a digital twin module, a prediction and inference module, an intervention decision-making module, and a self-correction module. Through real-time data acquisition, digital twin generation, spatiotemporal causal graph prediction, and counterfactual intervention strategy optimization, proactive identification and optimal intervention of traffic risks can be achieved.

Benefits of technology

It achieves the ability to adapt to dynamic traffic environments, enhances the initiative and accuracy of traffic risk management, ensures the reliability and optimality of intervention decisions, avoids blind management, and forms a self-optimizing closed-loop control system.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to the field of intelligent transportation, and discloses an expressway intelligent monitoring and management system, method and electronic equipment. The system collects expressway global space-time data to construct a digital twin body synchronized with the physical world. In the twin body, potential risks are identified through prediction and deduction based on a space-time causal graph. In response to the risks, an optimal intervention strategy is generated through counterfactual deduction and executed, and the prediction intervention effect is recorded. After the intervention, the real traffic state and the prediction effect are compared, the counterfactual error is calculated, and the space-time causal graph is dynamically self-corrected accordingly. The application integrates perception, prediction, decision-making, execution and feedback into an adaptive control loop, introduces a self-correction mechanism based on counterfactual error, enables the system to continuously learn and evolve from interaction with the physical world, and solves the problems of model solidification and poor adaptability of traditional traffic management systems.
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