摘要
本文针对智慧城市发展中对实时数据驱动决策的迫切需求,设计了一套城市大数据实时可视化系统。系统采用“云—边—端”级分布式架构,通过多源异构数据融合框架、混合渲染引擎、改进的ST-ResNet模型等关键技术,解决了传统可视化系统在动态性、多维融合与交互性上的不足。在某城市智慧交通应用中,系统实现了高峰期平均车速提升38%、核心区域通行延误减少37%等显著成效,为城市管理提供了精准决策支持。研究成果为数字孪生与城市大脑的深度融合提供了有效的技术路径。
This paper addresses the urgent need for real-time data-driven decision-making in smart city development by designing an urban big data visualization system.The system adopts a"cloud-edge-end"threetier distributed architecture,utilizing key technologies such as a multi-source heterogeneous data fusion framework,hybrid rendering engine,and an improved ST-ResNet model to overcome limitations in dynamism,multidimensional fusion,and interactivity of traditional systems.Applied in a smart traffic scenario in a city,the system achieved a 38%increase in average peak-hour vehicle speed and a 37%reduction in core area delays,providing precise decision support for urban management.This research offers an effective technical pathway for integrating digital twins with urban brains.
作者
武建军
WU Jianjun(Haigang District Urban Management Command and Dispatch Center,Qinhuangdao Hebei 066000)
出处
《软件》
2025年第4期145-147,共3页
Software
关键词
智慧城市
大数据可视化
实时渲染
云-边-端架构
时空数据融合
smart city
big data visualization
real-time rendering
cloud-edge-end architecture
spatiotemporal data fusion
作者简介
武建军(1969-),男,本科,高级工程师,研究方向:大数据。
