CN104462244B - A kind of smart city isomeric data sharing method based on meta-model - Google Patents

Abstract

The invention discloses a metamodel-based heterogeneous data sharing method for smart cities, including 1: constructing a meta-model modeling framework for urban data based on the MOF meta-modeling theory; 2: constructing urban data according to urban data characteristics and description requirements Meta-model general element set; 3: According to the basic classification of urban data and the expansion mode of special element set, develop the special element set of data meta-model; 4: Based on XML template modeling, form the formal method of meta-model general element set and special element set; 5: Realize network directory service and data service, and establish an open urban data meta-model registration and management platform; 6: According to the actual application needs of users, design an urban data fine-grained discovery interface and obtain data. The present invention provides users with an open and standardized urban heterogeneous data sharing solution, provides support for online access, subsequent processing and collaborative application of urban heterogeneous data, and is an efficient, practical and reliable method for urban heterogeneous data sharing .

Description

A Metamodel-based Heterogeneous Data Sharing Method for Smart City

technical field

The invention belongs to the technical field of smart city information sharing and interoperability, and relates to a method for sharing urban heterogeneous data, in particular to a metamodel-based smart city heterogeneous data sharing method.

Background technique

With the continuous development of cities, urban problems such as population expansion, traffic congestion, environmental pollution, and energy shortages continue to increase, and the concept of smart cities has emerged as the times require. Smart city is based on the scientific concept of urban development, using a new generation of information technology, based on the comprehensive perception and interconnection of ubiquitous information, to realize the seamless connection and collaborative linkage between people, things and urban functional systems. Adaptation and self-optimization, so as to make intelligent responses to various urban needs such as people's livelihood, environmental protection, public safety, urban functions, business activities, etc., and form a safe, convenient, efficient and green urban form with endogenous power for sustainable development .

At this stage, with the rapid development of my country's smart city construction, the volume and types of urban data have shown explosive growth, the data sources are rich and diverse, and the characteristics of heterogeneous data are obvious, especially a large amount of streaming media data (such as traffic video, security monitoring, etc. ) and real-time dynamic urban real-time observation data (such as weather, environmental protection, positioning and other sensor data) are emerging, and effective management and utilization of these data has become a major challenge. At the same time, since the informatization of urban operation and management in our country is a process of gradual improvement, systems that are implemented in different periods for different application fields, due to the operating environment, database system, service mode, information coding rules, business process definition, etc. Different standards are implemented in different aspects, systems cannot be interconnected, and information cannot be widely shared. When faced with the cross-field and multi-data collaborative application of specific cities, urban data resources present a situation of both more and less: cities have a large amount of archived data in various ways and real-time data of operating status continuously generated. Due to different data sources, different data formats, vague descriptions of data content, uneven data quality, and huge differences in data acquisition methods, etc., data cannot be fully understood and widely used for specific cross-domain and multi-data collaborative applications. acquisition and effective use.

Therefore, in the face of such massive, heterogeneous, and scattered urban data, how to uniformly and comprehensively describe the huge and complex heterogeneous data and conduct open integrated management to provide users with accurate, convenient, and effective data discovery and acquisition interfaces, Promoting the extensive sharing and interoperability of urban data has become the core problem to be solved urgently in the construction of smart cities at this stage.

In terms of Internet data sharing, traditional data sharing technologies mainly include semantic annotation and Web API technology. The standards of semantic annotation technology mainly include Microformat, RDFa, Microdata, etc. However, semantic annotation technology has the disadvantages of narrow application range and limited description ability. Web API technology is currently the most widely used form of data sharing applications. The disadvantage is that the open interface is inconsistent, and the returned data is not related, so the interconnection between data cannot be realized. In addition, Tim Berners-Lee first proposed the concept of Linked Data in 2006. It is one of the themes of the Semantic Web and describes methods for publishing, sharing, and connecting various resources on the Web through linkable URIs. LinkedData is the implementation of the data description framework in the Semantic Web. It is a framework that interconnects data by publishing structured data to improve the value of data applications. Its description of data is relatively simple, but its technology is complex and difficult to maintain. Fan et al. proposed an idea of expanding the Internet of Things - IOD (Internet of Data) compares data to entities in the Internet of Things, and uses data tags for data association, which is a new idea for data sharing. However, it is mainly limited to multimedia data and has problems such as content distortion and complex algorithms.

In terms of geographic information service sharing, OGC (Open GIS Consortium) proposed geographic information services such as Web Map Service (WMS), Web Feature Service (WFS), Web Geographic Coverage Service (WCS), and Tile Map Web Service (WMTS). Service standards. WMS can return corresponding maps (including raster forms such as PNG, GIF, JPEG, etc., or vector forms such as SVG and WEB CGM) according to user requests; WFS supports insertion, update, deletion, retrieval and discovery services for geographic elements; WCS provides a spatial raster layer containing geographic location information or attributes, rather than static map access. WMTS corresponds to the slice data access form. For example, to realize Tiandi stepless zoom operation, it is necessary to call WMTS service. Microsoft provides Internet Information Services (IIS, Internet Information Services), which publishes local resources in the form of network sites and provides quick access to local static data, such as multimedia data, such as videos, pictures, and texts. These network services only exist individually or together, and access can only be limited to one or several data forms, lacking unified discovery and access for multiple types of data.

In terms of real-time observation data sharing, the Open Geospatial Consortium (OGC) released the SOS (Sensor Observation Service) version 1.0 in 2006, and the Open Geospatial Consortium released the SOS 2.0 version in 2012. SOS provides an API for managing sensor registration and discovering sensor data. Through this service, customers can obtain the observation data of one or more sensors through the standard interface of SOS. The sensor observation service is suitable for recording data in the observation attribute-observation value format, and does not support other data types.

Based on comprehensive analysis, the current methods for urban heterogeneous data sharing have the following problems:

1) There is a lack of top-level design and multi-faceted detailed descriptions of multiple types of data. Various data tend to form information islands, and various data sources cannot be fully understood and discovered;

2) There is a lack of a cross-platform, universal data discovery and acquisition interface, which cannot satisfy users' fine-grained and precise discovery of data, and various data cannot be fully and effectively utilized;

3) There is a lack of a unified information integration management platform, and the interconnection and intercommunication between various data information systems cannot be realized, and the ability to coordinate actions cannot be formed.

Contents of the invention

In order to solve the above technical problems, the present invention proposes a method for sharing heterogeneous data in a smart city based on a meta-model.

The technical solution adopted in the present invention is: a method for sharing heterogeneous data in a smart city based on a meta-model, characterized in that it includes the following steps:

Step 1: Based on the meta-modeling theory of MOF (meta object facility), construct the urban data meta-model modeling framework;

Step 2: According to the characteristics and description requirements of urban data, construct the general element set of urban data meta-model;

Step 3: According to the basic classification of urban data and the expansion mode of the special element set, develop the special element set of the data meta model;

Step 4: Based on XML template modeling, form a formal method of meta-model general element set and special element set;

Step 5: Realize network directory service and data service, and establish an open urban data meta-model registration and management platform;

Step 6: According to the user's actual application requirements, design the city data fine-grained discovery interface and obtain the data.

As preferably, the specific realization of described step 1 includes the following sub-steps:

Step 1.1: Based on the MOF meta-modeling theory and according to the actual characteristics of urban data modeling, adopt induction and abstraction methods to construct a unified abstract syntax and a standard four-layer data meta-model modeling framework;

Step 1.2: According to the urban data sharing requirements and the essential characteristics of data in the context of smart cities, construct five meta-module classes of "label", "content", "quality", "distribution" and "reference" in the information description meta-model.

As preferably, the specific realization of described step 2 includes the following sub-steps:

Step 2.1: According to data characteristics and description requirements, on the basis of the five meta-module classes, develop ten meta-model components: DMM_identifacation, DMM_productTag, DMM_distrubutionFormat, DMM_dataQuality, DMM_transferType, DMM_distrubutorContact, DMM_spatialDimension, DMM_temporalDimension, DMM_contentType, DMM_metaReference;

Step 2.2: Refer to or expand the metadata elements in the existing geographic information standards, and develop a set of common elements of the data metadata model on the basis of the ten metamodel components.

As preferably, the specific realization of described step 3 includes the following sub-steps:

Step 3.1: According to the existing data classification, considering the characteristics of urban data, construct data classification under smart city, specifically including eight types of urban data: vector, image, terrain, 3D model, thematic attribute, real-time monitoring, streaming media, and document data;

Step 3.2: Specify the expansion mode of meta-model-specific element sets, investigate the characteristics of various types of data, take the meta-model "five-tuple" framework as the core, and take the "ten meta-model components" as the boundary to construct data on the basis of general element sets Metamodel special element sets, forming eight types of data metamodel special element sets.

As preferably, the specific realization of described step 4 includes the following sub-steps:

Step 4.1: Based on the meta-model framework and each element set, establish a UML class diagram, and use XML coding technology to design a formal template for a general element set and a special element set;

Step 4.2: Load the formalized template of the corresponding type of meta-model, and according to the characteristics of various aspects of the specific data set, establish the instance of the data meta-model through the block-by-block and hierarchical establishment of the formalized process of wizard-style modeling.

As preferably, the specific realization of described step 5 includes the following sub-steps:

Step 5.1: Realize network directory service and data service, register the completed metamodel instance and publish data service;

Step 5.2: Add, delete, modify, and update operations on the registered meta-model instance management.

As preferably, the specific realization of described step 6 includes the following sub-steps:

Step 6.1: According to the actual application needs of users, design a combined fine-grained discovery interface including five aspects of "data label", "data content", "data quality", "data distribution" and "data reference" at different levels;

Step 6.2: Analyze the data meta-model provided by the search and return, obtain information on various aspects of the data, and obtain the required data through data services.

The present invention has the following advantages and positive effects:

(1) Realize a unified and comprehensive description of urban heterogeneous data, which is convenient for network sharing. By modeling multi-source heterogeneous urban data, it fully reflects the information on data identification, content, quality, distribution, reference, etc., and fully expresses and uniformly describes the public attributes of the data; it designs a dedicated element set expansion mode to support Expression of differential information of various data types. Realize the unified expression of various aspects of heterogeneous and massive urban data, while meeting the needs of diverse representations;

(2) Realize the fine-grained discovery of urban heterogeneous data and improve the efficiency of data use. Based on the multi-angle and multi-level retrieval of the data meta-model, available data sets that meet specific data requirements can be obtained, and efficient and accurate discovery of data sets can be realized. He has made significant contributions to the traceability, evaluation, analysis and application of data.

Description of drawings

Fig. 1: is flow chart of the present invention.

Fig. 2: is the urban data meta-model modeling framework based on MOF meta-modeling established in step 1.1 in the embodiment of the present invention.

Fig. 3: is the general framework of the data meta-model established in step 1.2 in the embodiment of the present invention.

Fig. 4: is the city data classification established in step 3.1 in the embodiment of the present invention.

Fig. 5: It is the data metamodel-specific element set extension mode established in step 3.2 of the embodiment of the present invention.

Fig. 6 is a UML diagram of the data meta-model established in step 4.1 in the embodiment of the present invention.

Fig. 7 is an example of the data metamodel established in step 4.2 in the embodiment of the present invention.

detailed description

In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

In the context of smart cities, the present invention more comprehensively supports the description of all urban heterogeneous data, provides users with a way for urban heterogeneous data sharing and interoperability, and facilitates online access, subsequent processing and collaboration of urban heterogeneous data. The application provides support, and it is a more efficient, practical and reliable method for urban heterogeneous data sharing.

Please see Figure 1. This embodiment establishes a sharing mechanism for city data involved in emergency response to gas pipeline leakage and explosion in a smart city. The specific implementation process of the embodiment is as follows:

Step 1: Based on the MOF meta-modeling theory, construct the urban data meta-model modeling framework.

Step 1.1: Based on the meta-modeling theory of MOF (meta object facility), abstract and summarize the basic concepts and basic objects of urban heterogeneous data, and establish the meta-metamodel layer, meta-model layer, The modeling framework of the model layer and the instance layer (as shown in Figure 2).

Step 1.2: According to the sharing requirements of urban data integration management and collaborative call in the context of smart cities, analyze the essential characteristics of data, and focus on building (Distribution)" and "Reference (Reference)" and other information description meta-models of the five meta-module classes. These five meta-module classes basically cover all aspects of data sharing, and through subsequent inheritance and expansion, they can meet the needs of smart city management such as comprehensive emergency response and disposal for data sharing.

Step 2: According to the characteristics and description requirements of urban data, construct the general element set of urban data meta-model.

Step 2.1: According to data characteristics and description requirements, on the basis of the five meta-module classes, develop basic identification (DMM_identifacation), production identification (DMM_productTag), content type (DMM_contentType), spatial dimension (DMM_spatialDimension), temporal dimension (DMM_temporalDimension), Data quality (DMM_dataQuality), distribution format (DMM_distrubutionFormat), distribution contact (DMM_distrubutorContact), distribution method (DMM_transferType), reference information (DMM_metaReference) and other ten meta-model components (as shown in Figure 3);

Step 2.2: Reference or expand existing geographic information standards (such as geospatial dataset metadata content standards, ISO geographic information metadata standards, scientific database metadata standards, China sustainable development information sharing metadata standards, etc.) Metadata elements, based on the top ten metamodel components, develop a set of common elements of data metamodels, which contain metadata description items common to different city data types, such as basic names, keywords, dates, etc.;

Step 3: According to the basic classification of urban data and the expansion mode of the special element set, develop the special element set of the data meta model.

Step 3.1: According to the existing data classification, considering the characteristics of urban data, construct data classification under smart city applications, including eight types of urban data, including vector, image, terrain, 3D model, thematic attributes, real-time monitoring, streaming media, and document data (as shown in Figure 4);

Step 3.2: Specify the expansion mode of the special element set of the metamodel. Figure 5 shows the expansion mode of the special element set of the data metamodel, with the metamodel "five-tuple" framework as the core and the "ten metamodel components" as the boundary. On the basis of the element set, the special element set of data meta model is constructed to form eight types of special element sets of data meta model.

Step 4: Based on XML template modeling, form the meta-model general element set and special element set formalization method.

Step 4.1: Based on the meta-model framework and each element set, establish a UML class diagram (as shown in Figure 6), and then use XML coding technology to design a formal template for the general element set and the eight types of data-specific element sets, and store them in the template library of the system In , the template is convenient for the system to load the template to quickly and standardize the establishment of the required data meta-model instance;

Step 4.2: According to the specific data type, the system loads the formalized template of the corresponding type of meta-model, and according to the characteristics of various aspects of the specific data set, the data meta-model instance is established in blocks and layers through the formalized process of wizard-style modeling. For the specific application of smart city gas pipeline leakage and explosion emergency response, you can pre-create a wide range of urban data set meta-model instances, such as urban underground gas pipeline monitoring data, select the "real-time monitoring data" type to load the corresponding special element set template, and fill in the elements Collect all the information of the template to form a meta-model instance of gas pipeline monitoring data (as shown in Figure 7 is the vector data meta-model of bus lines), and so on, it is possible to establish a model that includes urban orthophotos, topographic maps, 3D models, pipelines, etc. Metamodel instance library for various types of data. These standard formalized model instances not only record information about various aspects of the data, but also include data acquisition methods.

Step 5: Realize network directory service and data service, and establish an open urban data meta-model registration and management platform.

Step 5.1: Register the established model instance to the directory service center of the system, which can realize centralized registration management of distributed data meta-model instances. According to the data type and the specific external acquisition method, establish a data acquisition service. For example, sensor observation service (SOS) can be established for real-time monitoring data; network element service WFS can be released for vector data; network coverage service (WCS) can be released for raster data, etc.;

Step 5.2: For the registered urban heterogeneous data meta-model instance, it can be managed comprehensively. The system provides operations such as adding, deleting, modifying, updating, etc., and also provides association and updating operations for data services, so that management can Logically manage all kinds of city data that are physically distributed.

Step 6: According to the user's actual application requirements, design the city data fine-grained discovery interface and obtain the data.

Step 6.1: According to the user's actual application requirements, design a combined fine-grained discovery interface at different levels including "data label", "data content", "data quality", "data distribution", and "data reference", and implement it in the system Display an interactive interface that enables users to obtain ideal data for specific needs. For example, in the emergency response process of gas leakage, different topics and different types of data collections that require a certain space and time range can be accurately retrieved through the data discovery interface;

Step 6.2: Analyze the data metamodel provided by the search and return to obtain data identification, content, quality, format, person in charge, etc., and obtain data through the included data services, such as those recorded by the gas pipeline monitoring data metamodel The Sensor Observation Service (SOS) can obtain real-time and historical monitoring data, and the corresponding vector data can be obtained through the recorded vector data service.

So far, the whole process of urban heterogeneous data sharing based on meta-model in smart city has been completed, involving meta-model proposal, model instance establishment, model instance management, model instance discovery and data acquisition. The proposed data meta-model covers all aspects of data information, and unified and formalized expression through XML coding, which realizes the comprehensive representation and unified expression of urban heterogeneous data information. At the same time, based on network registration service, sensor observation service and other The implementation of heterogeneous data management and sharing platform for services provides efficient tools for data integration and collaborative applications involved in various fields of smart city applications. In short, it is a more practical and reliable method with high efficiency in urban heterogeneous data sharing.

It should be understood that the parts not described in detail in this specification belong to the prior art.

It should be understood that the above-mentioned descriptions for the preferred embodiments are relatively detailed, and should not therefore be considered as limiting the scope of the patent protection of the present invention. Within the scope of protection, replacements or modifications can also be made, all of which fall within the protection scope of the present invention, and the scope of protection of the present invention should be based on the appended claims.

Claims (3)

1. A metamodel-based method for sharing heterogeneous data in smart cities, characterized in that it comprises the following steps: Step 1: Based on the meta-modeling theory of MOF (meta object facility), construct the urban data meta-model modeling framework; The specific implementation includes the following sub-steps: Step 1.1: Based on the MOF meta-modeling theory and according to the actual characteristics of urban data modeling, adopt induction and abstraction methods to construct a unified abstract syntax and a standard four-layer data meta-model modeling framework; Step 1.2: According to the urban data sharing requirements and the essential characteristics of data in the context of smart cities, construct five meta-module classes of "label", "content", "quality", "distribution" and "reference" in the information description meta-model; Step 2: According to the characteristics and description requirements of urban data, construct the general element set of urban data meta-model; The specific implementation includes the following sub-steps: Step 2.1: According to data characteristics and description requirements, on the basis of the five meta-module classes, develop ten meta-model components: DMM_identifacation, DMM_productTag, DMM_distrubutionFormat, DMM_dataQuality, DMM_transferType, DMM_distrubutorContact, DMM_spatialDimension, DMM_temporalDimension, DMM_contentType, DMM_metaReference; Step 2.2: Refer to or expand the metadata elements in the existing geographic information standards, and develop a set of common elements of the data metamodel on the basis of the ten metamodel components; Step 3: According to the basic classification of urban data and the expansion mode of the special element set, develop the special element set of the data meta model; The specific implementation includes the following sub-steps: Step 3.1: According to the existing data classification, considering the characteristics of urban data, construct data classification under smart city, specifically including eight types of urban data: vector, image, terrain, 3D model, thematic attribute, real-time monitoring, streaming media, and document data; Step 3.2: Specify the expansion mode of meta-model-specific element sets, investigate the characteristics of various types of data, take the meta-model "five-tuple" framework as the core, and take the "ten meta-model components" as the boundary to construct data on the basis of general element sets Meta-model special element sets, forming eight types of data meta-model special element sets; Step 4: Based on XML template modeling, form a formal method of meta-model general element set and special element set; The specific implementation includes the following sub-steps: Step 4.1: Based on the meta-model framework and each element set, establish a UML class diagram, and use XML coding technology to design a formal template for a general element set and a special element set; Step 4.2: Load the formalized template of the corresponding type of meta-model, and according to the characteristics of various aspects of the specific data set, establish the instance of the data meta-model through the formalized process of wizard modeling in blocks and layers; Step 5: Realize network directory service and data service, and establish an open urban data meta-model registration and management platform; Step 6: According to the user's actual application requirements, design the city data fine-grained discovery interface and obtain the data. 2. The metamodel-based smart city heterogeneous data sharing method according to claim 1, characterized in that: the specific realization of the step 5 includes the following sub-steps: Step 5.1: Realize network directory service and data service, register the completed metamodel instance and publish data service; Step 5.2: Add, delete, modify, and update operations on the registered meta-model instance management. 3. The metamodel-based smart city heterogeneous data sharing method according to claim 1, characterized in that: the specific realization of the step 6 includes the following sub-steps: Step 6.1: According to the actual application needs of users, design a combined fine-grained discovery interface including five aspects of "data label", "data content", "data quality", "data distribution" and "data reference" at different levels; Step 6.2: Analyze the data meta-model provided by the search and return, obtain information on various aspects of the data, and obtain the required data through data services.