Multi-sensor Integration Technology And Application Based On Ontology Theory

It’s not only slow to get the data information in the traditional garden green land management,but also cannot solve the problem in time when we found it.The Internet of Things technology is introduced to achieve delicacy management,and a large number of sensor devices are embedded in the garden green space to collect relevant data,which makes the data generated in the garden green space grow explodes and the speed of obtaining information from the data changes in a qualitative way.Based on a large amount of data in different types,people build different platforms or systems for different purposes to obtain the required information.These different data types and systems,depending on the purpose of use or the different understandings of the operators,lead to the emergence of semantic heterogeneous problems.It is difficult for the independent systems to share information,interoperate with each other,and users cannot understand each other.Moreover,the traditional keyword retrieval method has been unable to satisfy the needs of people to obtain more relevant information from the data when search the data information.The ontology theory provides a new and feasible way to solve the above problems.In this paper,the semantic heterogeneity and data information sharing problems are solved by constructing the ontology of green space and semantic similarity matching research,and the retrieval conditions are extended to query according to the similarity matching,so as to obtain the deep information to meet the needs of users.This paper mainly studies the integration and application of multisensor based on ontology,and the main work includes the following aspects:(1)Ontology-based database design.First,the hybrid ontology method is used to extract the concept of the entity in the garden green space,and then based on each independent database to be set,the corresponding local ontology is constructed to form a link between data and local ontology.Finally,mapping rules between form the link between global ontology and local ontology.In this paper,ontology editing software is used to describe the classes and attributes of the ontology of garden green space,and to reason the deep conceptual relations.The formed logical relationship is used in database design to meet the design specifications,thereby generating database script files.(2)Ontology similarity matching.The traditional ontology similarity algorithm is improved according to the characteristics of the object in this study.The implicit relationship at the edge of the conceptual relationship is included into the scope of the ontology similarity calculation mainly from two aspects of the structure relation similarity and the amount of information similarity.The type of conceptual relation edge is redefined and the weight of implicit relation edge type is calculated;The calculation formula of edge weight of node depth relation is improved and the time complexity of edge weight calculation of node depth relation is reduced;The similarity degree of implicit relation edge information content is considered.Through the comparative analysis of the experiment,the improved algorithm has been verified,and the accuracy of the object studied in this paper has been improved to a certain extent,which is in line with the actual cognition.(3)Integrated case application.In this paper,there are two functional modules of the system applied in detail,which are data statistics module and semantic retrieval module.The data statistics module is based on the relationship between the data provided by the garden green space ontology constructed,so that the data can be retrieved more quickly during the visual display.Semantic retrieval module,using ontology similarity to extend the semantic retrieval conditions,the query results not only return the information required by the user,but also supplement the relevant correlation information,improve the recall rate.This paper uses ontology technology to apply it in the management of gardens and green spaces,which effectively solves the problems of management confusion and semantic heterogeneity caused by large amounts of data,and realizes information interoperability.The traditional similarity algorithm has been improved to make the calculation result more consistent with the ontology of the garden field and make the matching between data information and concepts more accurate.