Abstract
This study presents an in-depth study and analysis of IoT semantic association and decision-making using a partial differential fuzzy unsupervised approach. It focuses on a semantic annotation framework for device metadata and a knowledge base construction method to further improve the interoperability of IoT domain knowledge by building a unified IoT domain knowledge base and designing and implementing a semantic IoT knowledge management and application generation system. The main proposal is an IoT generic domain ontology, which reuses the existing excellent ontologies of IoT as much as possible, extracts the commonly used concepts of the domain and combines them, and provides a unified semantic template for IoT applications. On the other hand, by applying the entity linking technique to the extension of the knowledge base and linking the structured metadata of devices to the corresponding entities of the background knowledge base, the domain knowledge base can be made to share the rich background knowledge. At the same time, the interoperability of heterogeneous IoT metadata between applications is enhanced by unifying data and concepts from different device applications to the same background knowledge base through entity alignment techniques. The semantic representation of events applicable to IoT application scenarios is investigated, and an IoT event ontology for representing abstract events and event relationships in IoT is designed; next, a domain ontology with IoT sensing and control event representation capability is constructed based on the IoT event ontology, in which the typical domain ontology (SSN) that can be used for IoT applications is followed by the ontology reuse principle is improved and extended to support the description of event types and interevent relationships, and the IoT event model is associated with the improved IoT base ontology through an ontology alignment approach. Finally, the IoT sensing and control ontology are validated by semantic modeling of device composition, component relationships, and operational processes based on the IoT sensing and control ontology.
Highlights
Internet of things (IoT) extends and extends the connotation of the internet connection to any things and things and is a network that enables things to establish communication connection and information sharing with the internet based on standard protocols through terminal information sensing devices such as infrared sensors, radiofrequency identifiers, laser scanners, and global positioning systems to achieve positioning, monitoring, and management, with the goal of the interconnection of all things. e core elements of internet of things (IoT) can be summarized as terminal sensing, network communication, and application services, and the three elements reflect the essential characteristics of IoT interconnection of everything [1]
With the rapid expansion of the application scope of IoT and the gradual deepening of the application degree of domain integration, new scenarios and new demands are constantly generated, and new theories and technologies are proposed one after another around the three elements of IoT, which promote the rapid development of the IoT field [2]. en, two nonlinear partial differential equation approximation solution methods are applied to the prediction model of the flow field, and the research on the prediction identification of the medium physical parameters in the flow field and the prediction of the flow field development trend is carried out, which proves
We study how to use the partial differential equation learning model, combined with the characteristics of remote sensing images, and make a series of adaptations to the original partial differential equation learning model, which is mainly aimed at processing natural images, so that the model can process remote sensing images that are very different from natural images. e research goal of using the same framework model to deal with many different remote sensing image processing problems by using machine learning theory is achieved
Summary
Internet of things (IoT) extends and extends the connotation of the internet connection to any things and things and is a network that enables things to establish communication connection and information sharing with the internet based on standard protocols through terminal information sensing devices such as infrared sensors, radiofrequency identifiers, laser scanners, and global positioning systems to achieve positioning, monitoring, and management, with the goal of the interconnection of all things. e core elements of IoT can be summarized as terminal sensing, network communication, and application services, and the three elements reflect the essential characteristics of IoT interconnection of everything [1]. Internet of things (IoT) extends and extends the connotation of the internet connection to any things and things and is a network that enables things to establish communication connection and information sharing with the internet based on standard protocols through terminal information sensing devices such as infrared sensors, radiofrequency identifiers, laser scanners, and global positioning systems to achieve positioning, monitoring, and management, with the goal of the interconnection of all things. With the development of IoT technology, sensing devices are popular in all occupations and have a great impact on our work and life, such as intelligent transportation systems; people can use intelligent terminals to obtain various conditions of the road in time, to know whether the road ahead is blocked, for example, smart home system; and people can use various sensing devices to obtain home information, to carry out remote monitoring, remote operation, security management, etc. The relationship between artificial intelligence, blockchain, big data, cloud computing, and IoT will be rationalized to build a new, ubiquitous, and intelligent ICT (information, communication, and technology) infrastructure that can be applied to the whole society and industry
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