Intelligent Recommendation Of Safety Knowledge For Worker In Construction

Safety in construction has always been a major challenge plaguing practitioners,scholars,and society.Safety training is one of the direct and effective means to improve the safety knowledge level of workers and reduce the occurrence of unsafe behaviors.Existing safety training methods ignore the influence of individual characteristics,such as learning style and skill level,on the effect of safety training.As a result,the safety knowledge that individual workers need and lack cannot be adaptively presented in a receptive way.To this end,this research proposes an adaptive learning-based approach for the intelligent recommendation of safety knowledge for workers in construction.The main research contents are as follows:(1)Construction of worker safety knowledge domain model.Based on the analysis of construction safety standards,accident reports and other safety knowledge carriers,a semantic ontology model for worker safety knowledge learning is constructed.To automatically extract safety knowledge triples from texts containing construction safety knowledge,based on the CASREL model,a CL-CASREL model integrating unsupervised learning and supervised learning is proposed.The experimental results show that the precision,recall and F1-score are increased by 9.9%,5.0% and 7.0%,respectively.The graph database Neo4 j is then used to store and update the extracted safety knowledge triples to construct a visual safety knowledge graph.As a result,the construction of the worker safety knowledge domain model is realized,which provides a data basis for the adaptive intelligent recommendation of safety knowledge.(2)Construction of worker safety knowledge learner model.The typical composition of personalized characteristics in the learner model in the field of education and the distinctive features of workers are expounded.Therefore,the adaptive source of the learner model is clarified,including the type of work,learning style,cognitive level and skill level.For the feature description and acquisition of the adaptive source,the Solomon Learning Style Scale is used to obtain workers’ learning style,and the safety knowledge test is applied to assess workers’ cognitive level.Making use of the spatiotemporal information contained in video frames,an approach based on computer vision and deep learning is proposed to monitor the workers’ skill level.As a result,the construction and dynamic update of the worker safety knowledge learner model is realized,which provides a basis for the adaptive intelligent recommendation of safety knowledge.(3)Construction of worker safety knowledge adaptive engine based on multimodal data.A framework process for safety knowledge recommendation of adaptive engine is first established to describe the relationship between the characteristics of workers and the recommendation of training form,training path and training content.Considering the differences in workers’ cognitive need and learning style,intelligent recommendation algorithms for safety knowledge of different modalities is developed,including K-BERT for the recommendation of text training materials based on text,K-SGRAF for the recommendation of image training materials based on text,and DOLG for the recommendation of image training materials based on an image.By fusing the safety knowledge semantic information contained in the domain model,data and knowledge fusion-driven algorithms(K-BERT and K-SGRAF)exhibit superior performance than datadriven algorithms(BERT and SGRAF).In this way,the construction of worker safety knowledge adaptive engine is realized,which can support the intelligent recommendation and adaptive presentation of safety knowledge.(4)Design and application of worker safety knowledge adaptive learning system.Based on the construction of worker safety knowledge domain model,learner model and adaptive engine,the mobile and web terminal of the worker safety knowledge adaptive learning system are designed,developed,and applied.Comparing changes in safety knowledge learning status of workers with adaptive learning system and workers with nonadaptive learning system,it verifies that the adaptive learning-based safety knowledge intelligent recommendation method improves workers’ cognitive level and skill level.This research is of great significance for reducing unsafe behaviors on construction sites,improving the level of safety management,and promoting the high-quality development of the construction industry.