摘要
为支撑城市数字化转型,解决目前智慧城市数据不能满足多领域兼容覆盖的需要、数据孤岛等问题,本文提出融合城市动静态多源数据、城市数据运算方案和数据组织管理方案的城市空间数字底座的概念,进而提出一种依托本体框架的城市空间数字底座架构,以多源多模知识图谱为核心来管理城市治理的数据模块、数据运算模块、数据可视化模块和城市服务模块,使得所有数据和事件、行为都在同一框架与协议下运作.本架构有三个特点:①依托本体框架,构建语义解释器覆盖领域全要素,保证数据结构完整和质量可靠;②数据与业务脱钩,原始数据从具体事物中抽象出来,重构为知识单元,适度脱敏推动数据融合;③空间运算升级为知识运算,利用知识图谱通过知识推理、知识发现等来优化传统计算,拓展服务边界及节省运算资源.通过知识图谱的管理架构,可以保证城市空间数字底座的数据质量可靠性、完整性、可解释性和共享性,确保了空间数字底座对城市空间信息服务的支撑能力.
Abstract
The process of urban digitalization is constantly advancing alongside city development.The smart city represents a significant achievement of urban digitalization at this stage,having made notable progress by vertically building data foundations and applications in specific fields.However,as urban governance requirements become more complex,necessitating the integration of multi-field data,issues such as low data quality and data island have emerged from previous smart city data governance models.To support the digital transformation of cities and address various challenges in past smart city data governance,this paper proposes the concept of urban spacial digital base(USDB).USDB integrates urban dynamic and static multi-source data,urban data computing schemes,and data organization management scheme.On the basis of this concept,a new data organization management architecture is proposed.This architecture relies on the ontology framework and utilizes multi-source and multi-model knowledge graphs as the core for managing data modules,data computing modules,data visualization modules,and urban service modules for urban governance.This ensures that all data,events,and behaviors operate under the same framework and protocol.The architecture possesses three key characteristics:① Leveraging the ontology framework,a semantic interpreter is constructed to cover all domain elements,ensuring the integrity and quality of the data structure.② Data is decoupled from business operations;original data is abstracted from concrete things,reconstructed into knowledge units,and appropriately desensitized to facilitate data collaboration.③ Spatial computing is elevated to knowledge operations,utilizing knowledge graphs to optimize traditional computing through knowledge reasoning and knowledge discovery,thereby expanding service boundaries and conserving computing resources.Finally,two practical examples of urban spatial digital bases are provided:① The spatial digital base supports the operation of the Shanghai Platform for Common Geospatial Information Services,dynamically displaying urban operational conditions and achieving refined urban grid comprehensive management.② Utilizing the Shanghai urban spatial digital base,road network knowledge graphs and road condition knowledge graphs are constructed using road network data and expressway road condition change corpus data.Through association and fusion of these graphs,aggregation of multi-source and multi-modal data,such as text data and geographic data,as well as the embedding of knowledge,are achieved.The management architecture of the knowledge graph inherits construction achievements from existing smart cities,reuses existing big data,and incorporates non-spatial data,such as socio-economic data,into the urban data governance framework.This provides additional dimensions for urban governance while using ontology frameworks to ensure data quality and upgrading existing spatial data operations to relationships in knowledge graphs to enhance the responsiveness of urban governance services.It provides a good data management paradigm for the process of urban digitization.As a result,the urban spacial digital base guarantees data quality,integrity,interpretability,and sharing,while ensuring its support for urban spatial information services.
基金项目
上海市科委社会发展科技攻关计划(21DZ1204100)
NETL
NSTL
万方数据