基于深度卷积神经网络的建筑施工进度信息集成方法  

Integration Method of Building Construction Progress Information Based on Deep Convolutional Neural Network

作　　者：李健 邱坤 LI Jian;QIU Kun(Zaozhuang Vocational College,Shandong Zaozhuang 277800,China)

机构地区：[1]枣庄职业学院,山东枣庄277800

出　　处：《长江信息通信》2025年第1期70-72,共3页Changjiang Information & Communications

摘　　要：针对建筑施工进度信息集成中的分类冗余问题,文章提出基于深度卷积神经网络的集成方法。采用RFID标签唯一标识信息单元,避免冗余。通过构建深度卷积神经网络模型,实现动态信息共享。中间件队列和信息类别选择确保实时性。实验证明,该方法集成度高、无冗余,适用于复杂施工环境,具有推广价值。The article proposes an integration method based on deep convolutional neural networks to address the issue of classification redundancy in the integration of construction progress information.Using RFID tags to uniquely identify information units and avoid redundancy.By constructing a deep convolutional neural network model,dynamic information sharing can be achieved.The selection of middleware queues and information categories ensures real-time performance.Experimental results have shown that this method has high integration,no redundancy,and is suitable for complex construction environments,with promotional value.

关 键 词：深度卷积神经网络 建筑施工 施工进度 信息集成方法 信息集成编码 

分 类 号：TU17[建筑科学—建筑理论]