面向城市绿化精细管理的低密度树木数字孪生模型——以树木风灾风险管理为例  

A low-density tree digital twin model for refined urban greening management:a case study of tree wind disaster risk management

作　　者：魏云琦 王旸 尹豪[1] Wei Yunqi;Wang Yang;Yin Hao(School of Landscape Architecture,Beijing Forestry University,Beijing 100083,China)

机构地区：[1]北京林业大学园林学院,北京100083

出　　处：《北京林业大学学报》2025年第3期139-150,共12页Journal of Beijing Forestry University

基　　金：北京林业大学热点追踪项目“城市人居环境植物景观资源应用水平与提升策略研究”(2022BLRD05);北京市共建项目专项资助(2019GJ-03)。

摘　　要：【目的】针对传统理想化树木数字模型难以精细呈现树木真实形态的问题,本研究提出一种精细化树木数字孪生模型的生成方法,为智慧城市技术在城市绿化精细化管理中的应用提供高效、准确的模型支持。【方法】本研究以北京市某高校建筑旁的16棵国槐树为对象,构建精细化树木数字孪生模型。首先,通过循环应用DBSCAN算法对单体树木点云进行分割;其次,结合降维升维思想,利用二维点云的α-shapes算法提取三维树木点云的外轮廓;然后,通过上采样最近插值法生成真实树木的三维mesh网格,并利用Grasshopper进行模型平滑处理;最后,在Revit平台上添加模型参数信息,完成树木数字孪生模型的创建。模型质量通过CloudCompare软件从树木形态参数还原性和点云契合度两方面进行评估,并利用Phoenics软件对比分析其与理想化树木数字模型在CFD风模拟中的表现。【结果】16棵形态各异的国槐数字孪生模型与点云距离的平均误差为–3.06 cm,证明该模型能够精确保留树木的独特形态特征。在强风条件下,两种模型的风速模拟结果差异显著,最大可相差97.23%。【结论】该模型为城市绿化精细化管理提供了有效的决策工具,有助于拓展智慧城市技术在城市绿化管理中的应用场景。然而,由于DBSCAN点云分割算法的局限性,本研究方法主要适用于低密度树木群落建模。对于树冠重叠度较高的高密度植物群落,仍需探索新的建模方法以进一步优化模型的适用性。[Objective]Traditional idealized tree models struggle to accurately represent the real shape of trees.This study proposes a method to generate a refined tree digital twin model,providing an efficient and accurate model support for the application of smart city technologies in urban greening management.[Method]This study constructed a refined digital twin model for 16 Chinese scholar trees(Sophora japonica)located beside a university building in Beijing.First,the single tree point cloud was segmented by cyclically applying the DBSCAN algorithm.Second,theα-shape algorithm of 2D point clouds,combined with the idea of dimensionality reduction and augmentation,was used to extract the outer contour of 3D tree point clouds.Thirdly,the 3D mesh of real tree was generated using up-sampling nearest interpolation method,and then the model smoothing was carried out by Grasshopper.Finally,model parameters were added on the Revit platform to complete the creation of digital twin model.The model quality was assessed in terms of tree morphology parameter reproducibility and point cloud fit using CloudCompare software.Additionally,the performance of model in CFD wind simulation was compared with that of an idealized tree digital model using Phoenics software.[Result]The average error between digital twins of 16 diverse Chinese scholar trees and the point clouds was-3.06 cm,indicating that the model can accurately preserve the unique morphological characteristics of trees.Under strong wind conditions,the wind speed simulation results of the two models showed significant differences,with a maximum discrepancy of 97.23%.[Conclusion]The model provides an effective decision-making tool for the refined management of urban greening,and helps to expand the application scenarios of smart city technologies in urban greening management.However,due to the limitations of DBSCAN point cloud segmentation algorithm,the method in this study is mainly applicable to modeling low-density tree communities.For high-density plant communities with a hig

关 键 词：智慧城市 数字孪生 城市绿化 点云分割 3D建模 计算机仿真 风险评估 精细管理 

分 类 号：S731.2[农业科学—林学] TP391.9[自动化与计算机技术—计算机应用技术]