Construction Method of Cross-Domain Internet of Things Semantic Middleware for Smart City Infrastructure

Abstract

The present invention relates to a method for constructing a cross-domain Internet of Things semantic middleware for smart city infrastructure, which specifically includes the following steps: S1. Obtain multi-domain heterogeneous data collected by the device side of the Internet of Things according to the communication protocol; S2. Respectively construct sub-domain knowledge graphs according to multiple sub-domain systems corresponding to the multi-domain heterogeneous data, and convert the heterogeneous data into structured data; S3. Integrate the sub-domain knowledge graphs based on the middleware framework of WoT. There is a middleware L0 layer between the smart city platform and the system application of the Internet of Things as the cross-domain Internet of Things semantic middleware. The middleware L0 layer provides a unified access data interface to connect with the system application of the Internet of Things, and transmits the unified urban Internet of Things data to the smart city platform. Compared with the prior art, the present invention has the advantages of providing a unified access interface for data under different Internets of Things and solving the problem of data islands in cross-domain Internet of Things, etc.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things for urban facilities, and in particular to a method for constructing a cross-domain Internet of Things semantic middleware for smart city infrastructure. Background Art

[0002] The Internet of Things (IoT) is a large network that combines various things on the Internet. It collects various data required in the network in real time to achieve the interconnection and interoperability between things and between people and things.

[0003] With the in-depth development of the Internet of Things, there are more and more various devices in the IoT network, resulting in a large amount of multi-source and heterogeneous data, including unstructured, semi-structured, and structured data, which brings great difficulties to data processing, data fusion, and resource sharing. Traditional smart city infrastructure uses a closed-loop IoT system to manage, analyze, and control data. In the future, new smart cities tend to be more efficient in collaboration and sharing, which urgently requires the maximum degree of open sharing of data. This transformation involves topics such as the evolution of the IoT framework and IoT data sharing. Therefore, breaking data islands, understanding the semantic information of heterogeneous data generated by different devices, and realizing cross-domain sharing of data have become urgent problems to be solved in the field of the Internet of Things. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for constructing a cross-domain Internet of Things semantic middleware for smart city infrastructure to overcome the defect that the data processing method of the existing smart city infrastructure does not meet the requirements of more efficient collaboration and sharing between devices.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for constructing a cross-domain Internet of Things semantic middleware for smart city infrastructure specifically includes the following steps:

[0007] S1. Obtain multi-domain heterogeneous data collected by the device side of the Internet of Things according to the communication protocol;

[0008] S2. Respectively construct sub-domain knowledge graphs according to multiple sub-domain systems corresponding to the multi-domain heterogeneous data, and convert the heterogeneous data into structured data;

[0009] S3. The sub-domain knowledge graphs are integrated based on the middleware framework of WoT. A middleware L0 layer is provided between the smart city platform and the system application of the Internet of Things as a cross-domain Internet of Things semantic middleware. The middleware L0 layer provides a unified data access interface to connect with the system application of the Internet of Things, and transmits the unified urban Internet of Things data to the smart city platform.

[0010] The middleware L0 layer acts as a bridge between existing different systems or platforms, enabling efficient interaction between devices in different domains.

[0011] The cross-domain Internet of Things semantic middleware further includes a middleware L1 layer.

[0012] Furthermore, the middleware L1 layer is located between the system applications of the Internet of Things and the sub-domain knowledge graph of the Internet of Things.

[0013] Furthermore, the structures of both the middleware L0 layer and the middleware L1 layer are based on the Apache Jena framework. The Apache Jena framework provides a large number of APIs that support RDFS and OWL, and realizes the access and output of dynamic data.

[0014] Furthermore, Fuseki is provided as a SPARQL server or SPARQL endpoint in both the middleware L0 layer and the middleware L1 layer of the Apache Jena framework, providing SPARQL updates, queries, and modifications via the HTTP protocol.

[0015] Furthermore, Fuseki is connected to the TDB database, and the TDB database is connected to the query engine of the Apache Jena framework. The TDB database provides extremely high storage performance. Fuseki and the TDB data are highly integrated, including Jena's text queries and spatial queries, and also provide a business persistence layer.

[0016] Furthermore, the query engine of the Apache Jena framework is connected to the rule inference engine of the Apache Jena framework, and the rule inference engine is connected to Fuseki. The query engine of the Apache Jena framework supports the SPARQL RDF query language.

[0017] The sub-domain knowledge graph is constructed using a top-down construction method. The construction process includes ontology selection and development, ontology instantiation, and semantic annotation, using the existing structured knowledge base as the basic knowledge base, where the initial basic ontology uses the standardized oneM2M basic ontology.

[0018] Furthermore, the sub-domain knowledge graph includes a schema layer and a data layer. The schema layer is constructed according to the oneM2M model, and the data layer is constructed through ontology instantiation and semantic annotation.

[0019] Furthermore, the sub-domain knowledge graph uses the general M2M service layer platform access interfaces and standards provided by oneM2M to represent heterogeneous data from different sources in a general format.

[0020] Furthermore, the instances in the data layer are specifically triples that follow the RDF schema. The process of semantic annotation includes linking the resource information generated by IoT devices to the ontology established in the previous step, and finally creating an IoT knowledge graph from scratch. In the data layer, the URI is a string used to uniquely identify RDF resources, and various resources such as videos, images, and documents existing in the IoT can be uniquely identified by the URI. The URI includes the naming mechanism of the resource, the hostname of the resource, and the name of the resource itself.

[0021] During the integration of the sub-domain systems, if there is no semantic support system between the sub-domain knowledge graphs, the sub-domain systems are jointly constructed and a common ontology subset is set; if there is a single sub-domain system that already supports semantic functions while the other sub-domain system does not, the semantic functions of the non-supporting sub-domain system are set through the ontology of the supported sub-domain system or a vocabulary is established to achieve mapping between the systems; if both sub-domain systems already support semantic functions, a vocabulary is established to achieve mapping between the systems.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] 1. The present invention provides a middleware L0 layer between the smart city platform and the system application of the Internet of Things as a cross-domain IoT semantic middleware, which develops a lightweight, open, and highly adaptable middleware for specific scenarios, supports the mutual communication between different systems, and effectively reduces the repetitive work of independently developing subroutines for each module with similar functions.

[0024] 2. The present invention uses SPO triples that follow the RDF schema to uniformly describe the data obtained by sensors, and the relationships between the data are presented in the form of a graph, effectively solving the problem of difficult access to heterogeneous resources.

[0025] 3. The present invention introduces the L0 layer into the traditional IoT middleware framework and uses technologies such as Web API, gateway, blockchain, and RDFS to provide a unified access interface for data under different IoT scenarios, solving the problem of data islands in cross-domain IoT. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic flowchart of the present invention;

[0027] Figure 2 is a schematic structural diagram of the multi-layer semantic middleware framework based on the WoT knowledge graph of the present invention;

[0028] Figure 3 is a schematic structural diagram of the schema layer of the sub-domain knowledge graph of the present invention;

[0029] Figure 4Schematic diagram of the multi - layer semantic middleware for cross - domain intelligent buildings according to the present invention;

[0030] Figure 5 Schematic diagram of the implementation of the Internet of Things knowledge graph in the embodiment of the present invention;

[0031] Figure 6 Schematic diagram of the response time of the cross - domain management system under different numbers of users according to the present invention. Detailed implementation manners

[0032] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives detailed implementation manners and specific operation processes, but the protection scope of the present invention is not limited to the following embodiments.

[0033] Embodiment

[0034] As Figure 1 shown, a method for constructing a cross - domain Internet of Things semantic middleware for smart city infrastructure specifically includes the following steps:

[0035] S1. Obtain multi - domain heterogeneous data collected by the device side of the Internet of Things according to the communication protocol;

[0036] S2. Respectively construct sub - domain knowledge graphs according to multiple sub - domain systems corresponding to the multi - domain heterogeneous data, and convert the heterogeneous data into structured data;

[0037] S3. The sub - domain knowledge graphs are integrated based on the middleware framework of WoT. There is a middleware L0 layer between the smart city platform and the system applications of the Internet of Things as the cross - domain Internet of Things semantic middleware. The middleware L0 layer provides a unified access data interface to connect with the system applications of the Internet of Things, and transmits the unified city Internet of Things data to the smart city platform.

[0038] As Figure 2 shown, the middleware L0 layer acts as a bridge between existing different systems or platforms, and realizes efficient interaction between devices in different fields.

[0039] The cross - domain Internet of Things semantic middleware further includes a middleware L1 layer.

[0040] The middleware L1 layer is located between the system applications of the Internet of Things and the sub - domain knowledge graphs of the Internet of Things.

[0041] As Figure 4 shown, the structures of both the middleware L0 layer and the middleware L1 layer are based on the Apache Jena framework. The Apache Jena framework provides a large number of APIs that support RDFS and OWL, and realizes the access and output of dynamic data.

[0042] Both the middleware L0 layer and the middleware L1 layer are equipped with Fuseki as a SPARQL server or SPARQL endpoint of the Apache Jena framework, providing SPARQL updates, queries, and modifications via the HTTP protocol.

[0043] Fuseki is connected to the TDB database, which is connected to the query engine of the Apache Jena framework. The TDB database provides extremely high storage performance. Fuseki is highly integrated with the TDB data, including Jena's text queries and spatial queries, and also provides a business persistence layer.

[0044] The query engine of the Apache Jena framework is connected to the rule reasoner of the Apache Jena framework, and the rule reasoner is connected to Fuseki. The query engine of the Apache Jena framework supports the SPARQL RDF query language.

[0045] The sub-domain knowledge graph is constructed using a top-down construction method. The construction process includes ontology selection and development, ontology instantiation, and semantic annotation. The existing structured knowledge base is used as the basic knowledge base, and the initial basic ontology uses the standardized oneM2M basic ontology.

[0046] As Figure 3 shown, the sub-domain knowledge graph includes a schema layer and a data layer. The schema layer is constructed according to the oneM2M model, and the data layer is constructed through ontology instantiation and semantic annotation.

[0047] The sub-domain knowledge graph uses the general M2M service layer platform access interface and standards provided by oneM2M to represent heterogeneous data from different sources in a general format.

[0048] The instances in the data layer are specifically triples that follow the RDF schema. The process of semantic annotation includes linking the resource information generated by IoT devices to the ontology established in the previous step, and finally creating an IoT knowledge graph from scratch. In the data layer, the URI is a string used to uniquely identify RDF resources. Various resources such as videos, images, and documents existing in the IoT can be uniquely identified by the URI. The URI includes the naming mechanism of the resource, the hostname of the resource, and the name of the resource itself.

[0049] During the integration of the sub-domain systems, if there is no semantic support system between the sub-domain knowledge graphs, they jointly construct the sub-domain system and set a common ontology subset; if there is a single sub-domain system that already supports semantic functions while the other does not, the semantic functions of the non-supporting sub-domain system are set through the ontology of the supported sub-domain system or a vocabulary is established to achieve mapping between the systems; if both sub-domain systems already support semantic functions, a vocabulary is established to achieve mapping between the systems.

[0050] In this embodiment, specifically in implementation, an intelligent building is selected as one of the test cases. Suppose a house has two rooms, a living room, a kitchen, and a bathroom, a total of five spaces. Each space has a heating system, and one heating system may correspond to several heaters, such as air conditioners, fuel heaters, and so on. Usually, the national power grid provides tiered electricity prices, and as the electricity consumption increases, the electricity price gradually rises to improve energy utilization efficiency. Now, the residents of the intelligent building want to optimize the energy consumption of the house, need to know the power consumption of each household appliance in the house, and be able to adjust the status of each household appliance in a timely manner.

[0051] To implement this use case, the following different systems need to be integrated: 1) The heating system for each space; 2) The power consumption monitoring system for each appliance.

[0052] To achieve interoperability between different devices of the heating system and the power consumption monitoring system, it is necessary to reach an agreement in terms of interfaces and information modeling.

[0053] Therefore, as Figure 5 shown, the knowledge graph described above is used to model relevant information at the semantic level to achieve semantic interoperability. The information to be modeled includes: 1) Devices (units, instructions, modes, services, energy consumption profiles, etc.). 2) Spaces (status, instructions, modes, services, ambient temperature, etc.).

[0054] This embodiment uses response time and scalability to evaluate the Web system. The response time reflects the ability to provide services to users quickly and effectively. Virtual users are set up to simulate concurrency, a typical service request process is selected, and the response time is recorded. The shorter the response time, the faster the service is provided.

[0055] Three use case descriptions as shown in Table 1 are set, and the system response time, that is, the round-trip time (RTT), is tested in the case of a single user. The results show that the average RTT is within the range of 50 ms. Table 1 is as follows:

[0056] Table 1 Round-trip Time Results Table

[0057]

[0058] Scalability refers to the ability of the system to expand and grow. Latency is an important indicator for evaluating scalability. The lower the latency, the better the scalability of the system. The goal of scalability is to achieve the maximum throughput within an acceptable latency range.

[0059] In addition, with the growth of the number of users, the change in the response time of the test system is observed. The number of threads represents the number of virtual users, and the duration corresponding to the number of threads is set to 60 seconds. The number of users changes over time, and the response time of the system is as Figure 6 shown. The results indicate that as the number of users increases, the system response time also increases linearly, with the slope remaining constant, demonstrating that the Internet of Things based on the middleware of the present invention has high stability, ensuring the stable operation of the smart city infrastructure.

[0060] In addition, it should be noted that for the specific embodiments described in this specification, the names adopted may be different. The above content described in this specification is merely an illustrative example of the structure of the present invention. Any equivalent changes or simple changes made based on the structure, features, and principles conceived by the present invention are included within the protection scope of the present invention. Those skilled in the technical field to which the present invention pertains can make various modifications, supplements, or use similar methods to the specific examples described, as long as they do not deviate from the structure of the present invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

Claims

1. A method for constructing a cross-domain Internet of Things semantic middleware for smart city infrastructure, characterized in that Specifically, it includes the following steps: S1. Obtain multi-domain heterogeneous data collected by the device side of the Internet of Things according to the communication protocol; S2. Respectively construct sub-domain knowledge graphs according to multiple sub-domain systems corresponding to the multi-domain heterogeneous data, and convert the heterogeneous data into structured data; S3. The sub-domain knowledge graphs are integrated based on the middleware framework of WoT. There is a middleware L0 layer as a cross-domain Internet of Things semantic middleware between the smart city platform and the system application of the Internet of Things. The middleware L0 layer provides a unified access data interface to connect with the system application of the Internet of Things, and transmits the unified urban Internet of Things data to the smart city platform; The cross-domain Internet of Things semantic middleware further includes a middleware L1 layer; The middleware L1 layer is located between the system application of the Internet of Things and the sub-domain knowledge graph of the Internet of Things; The structures of the middleware L0 layer and the middleware L1 layer are both based on the Apache Jena framework; Both the middleware L0 layer and the middleware L1 layer are provided with Fuseki as a SPARQL server or a SPARQL terminal of the Apache Jena framework.

2. The construction method of a cross-domain Internet of Things semantic middleware for smart city infrastructure according to claim 1, wherein The Fuseki is connected to the TDB database, and the TDB database is connected to the query engine of the Apache Jena framework.

3. A method for constructing a cross - domain Internet of Things semantic middleware for smart city infrastructure according to claim 2, characterized in that, The query engine of the Apache Jena framework is connected to the rule inference engine of the Apache Jena framework, and the rule inference engine is connected to Fuseki.

4. A method for constructing a cross-domain Internet of Things semantic middleware for smart city infrastructure according to claim 1, characterized in that, The sub-domain knowledge graph is constructed by using a top-down construction method. The construction process includes ontology selection and development, ontology instantiation and semantic annotation, where the initial basic ontology uses the standardized oneM2M basic ontology.

5. A method for constructing a cross - domain Internet of Things semantic middleware for smart city infrastructure according to claim 4, characterized in that, The sub-domain knowledge graph includes a schema layer and a data layer. The schema layer is constructed according to the oneM2M model, and the data layer is constructed through ontology instantiation and semantic annotation.

6. A method for constructing a cross - domain Internet of Things semantic middleware for smart city infrastructure according to claim 1, characterized in that, During the integration process of the sub-domain systems, if there is no semantic support system between the sub-domain knowledge graphs, they jointly construct the sub-domain system and set a common ontology subset; if there is a single sub-domain system that already supports semantic functions while another sub-domain system does not support, the semantic functions of the non-supporting sub-domain system are set through the ontology of the supported sub-domain system or a vocabulary is established to achieve the mapping between the systems; if both sub-domain systems already support semantic functions, a vocabulary is established to achieve the mapping between the systems.

Images