With the increase of the types of urban management objects, the intelligent management of the whole city has become a matter of concern in various countries, and it is also one of the indispensable links in urban development. In the construction of cities all over the world, the intelligent and scientific management system has been used innovatively. We provide excellent facilities for transportation development, information exchange, and resource progress. The research on urban fine management based on multisource spatial data fusion is proposed. Aiming at the traffic problems in urban fine management, this paper proposes a deep network architecture based on multisource data fusion. Multisource spatial data fusion technology is used to analyze urban traffic data. Deep network architecture is used to improve the precision management status of a smart city and the accuracy of traffic condition prediction. Then, the convolution neural network technology is explored in the data fusion technology strategy. The research results show that the framework has the ability to deal with heterogeneous data and urban big data and can effectively improve the traffic management state in the construction of a smart city and effectively solve the complexity of urban fine management and processing efficiency in the construction of a smart city.

中文翻译：

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基于智慧城市建设的多源空间数据融合城市精细化管理

随着城市管理对象种类的增多，整个城市的智能化管理成为各国关注的问题，也是城市发展不可或缺的环节之一。在世界各地的城市建设中，智能化、科学化的管理系统得到了创新的应用。我们为交通发展、信息交流和资源进步提供优良的设施。提出基于多源空间数据融合的城市精细化管理研究。针对城市精细化管理中的交通问题，提出了一种基于多源数据融合的深度网络架构。多源空间数据融合技术用于分析城市交通数据。深度网络架构用于提高智慧城市的精准管理状态和交通状况预测的准确性。然后，在数据融合技术策略中探索了卷积神经网络技术。研究结果表明，该框架具有处理异构数据和城市大数据的能力，能够有效改善智慧城市建设中的交通管理状态，有效解决城市精细化管理和处理效率在智慧城市建设中的复杂性。一个智慧城市。