Artificial intelligence-based construction site safety risk identification and intervention system

By deploying an artificial intelligence system at the construction site, multimodal data is collected and analyzed in real time, risk feature vectors are generated, and intervention commands are executed. This solves the problems of information delay and bandwidth pressure in construction site safety management, and achieves efficient and real-time risk identification and intervention.

CN122414799APending Publication Date: 2026-07-17YANSHAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
YANSHAN UNIV
Filing Date
2026-04-17
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing technologies for safety management at construction sites suffer from information delays and network bandwidth pressures, resulting in long response times and low efficiency in risk management.

Method used

An AI-based construction site safety risk identification and intervention system is adopted. The system collects multimodal perception data in real time through a data acquisition module, uses a first neural network model to score and pre-classify risk urgency, generates key risk feature vectors, and uses a second neural network model to identify associated risks, generate risk reports, and execute corresponding intervention instructions, thereby achieving edge computing and cloud-based collaborative optimization.

Benefits of technology

It effectively alleviated network bandwidth pressure, improved information processing efficiency, ensured timely intervention and rapid response to high-urgency risks, achieved real-time and accuracy of the system, and improved resource utilization and scenario adaptability.

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Abstract

This invention relates to the field of safety risk identification technology, specifically disclosing an artificial intelligence-based safety risk identification and intervention system for construction sites. The system includes: a data acquisition module that collects personnel operation videos, images, and sensor data from multiple nodes within a key area, and performs preliminary processing to obtain raw multimodal perception data streams; a scheduling and analysis module that uses a first neural network model to perform real-time analysis of the raw multimodal perception data streams, generating risk urgency scores and pre-classification results, generating key risk feature vectors, and triggering corresponding data transmission paths; a risk identification module that receives the key risk feature vectors of the current data transmission path and uses a second neural network model to identify associated risks, generating a risk report; and a system that coordinates and optimizes the first and second neural network models based on the risk report; and an execution feedback module that receives and executes intervention instructions from the risk report through a preset hierarchical strategy library, performing intervention actions.
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