In order to prevent the construction edge fall accidents more timely and effectively,and make up for the shortcom-ings of existing intelligent early-warning methods in scene understanding,a near-miss identification method was proposed by integrating deep learning and semantic inference.This method first constructed a near-miss knowledge graph through neo4j,and then introduced the YOLOx model with a lightweight visual Transformer to identify the near-miss behavior of workers.An IoU calculation method for describing the spatial relationship was designed,and the Cypher inference language was used to carry out the near-miss inference.The results show that the average accuracy of identifying various elements of construction edge falling is over 91%,and both the accuracy of IoU calculation and near-miss inference are 100%.The model identifica-tion effect and near-miss inference effect are good.The method generally meets the identification requirements of accuracy and speed.The research results can provide reference for achieving the accurate identification and early-warning of construction edge falling near-miss behavior.
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