The present invention relates to the technical field of terminal devices of the Internet of Things, more specifically to a semantic method for terminal devices of the Internet of Things capable of analyzing the application characteristics of terminal devices of the Internet of Things, setting down the rules for building ontology base; analyzing and building initial ontology base by using the ontology base building tool; pre-processing information from network and sensors; collecting information uploaded by sensors, updating the initial ontology base, and expanding the ontology base by collecting information searched by network. The method establishes program plan by object-oriented mode, analyzes implementing results by using existing test data or simulated data, and realizes domain updating and expansion of sensor ontology according to specific applications. The whole development and evolution of sensor ontology is increasing gradually in a spiral form.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A semantic method for terminal devices of the Internet of Things, characterized in that: analyzing the application characteristics of terminal devices of Internet of Things, setting down the rules for building ontology base; analyzing and building initial ontology base by using the ontology base building tool; pre-processing information from network and sensors; collecting information uploaded by sensors, updating the initial ontology base, and expanding the ontology base by collecting information searched by network. 2. The semantic method for terminal devices of the Internet of Things as claimed in claim 1, characterized in that: the pre-processing includes ontology parsing and text processing. 3. The semantic method for terminal devices of the Internet of Things as claimed in claim 1, characterized in that: the ontology parsing further includes: parsing the concept information in the initial ontology base to get set of concepts; parsing instance information in the initial ontology base to get set of instances; and parsing the relationship information in the initial ontology base to get set of relationships. 4. The semantic method for terminal devices of the Internet of Things as claimed in claim 2, characterized in that: the text processing further includes: processing web texts and processing collected data texts. 5. The semantic method for terminal devices of the Internet of Things as claimed in claim 4, characterized in that: extracting web characteristics after web noise removal. 6. The semantic method for terminal devices of the Internet of Things as claimed in claim 4, characterized in that: the collected data text processing includes converting data format. 7. The semantic method for terminal devices of the Internet of Things as claimed in claim 1, characterized in that: the process of updating the initial ontology base includes: performing dynamic data matching and updating the set of instances in the initial ontology base when real-Lime dynamic measuring values sent by a sensor are collected; performing static data matching and updating the set of concepts and the set of relationships in the initial ontology base when intrinsic properly information values sent by a sensor are collected. 8. The semantic method for terminal devices of e Internet of Things as claimed in claim 1, characterized in that: the process of expanding the initial ontology base includes: filtering information searched by network to get network information highly similar to the ontology base; calculating the edition distance and context similarity to get information about words having a high correlation. 9. The semantic method for terminal devices of the Internet of Things as claimed in claim 8, characterized in that: the process of filtering information searched by network further includes: suppose the set of domain files in the quantity of n is SetD={D1, D2, . . . Dn}, and cj is any concept in the sensor ontology, then the correlation between file Di and concept cj is: DR  ( c j , D i ) = P  ( c j  D i ) ∑ i = 1 n  P  ( c j  D i ) where, P  ( c j  D i ) ≈ freq t , j ∑ j = 1 n  freq t , j , freqt,j are times of term cj appearing in Di, namely frequency, then the correlation between file Dj and the sensor ontology is: DRSenOnt=Σj=1mDR(cj,Di) m indicates the number of concepts in the sensor ontology; if the correlation between file Dj and the sensor ontology DRsenont is greater than the correlation coefficient of domain file θ, then the correlation of domain file is high, otherwise the correlation of domain file is low; where the correlation coefficient of domain file θ is the file variable input by users. 10. The semantic method for terminal devices of the Internet of Things as claimed in claim 8, characterized in that: the process of calculating the edition distance and context similarity further includes: give two words t1 and t2, according to the Sigmoid function as well as edition distance and context similarity, the conceptual matching degree of the two words is: Simconcept(t1,t2)=α.SimEditDist(t1,t2)+β.SimContext(t1,t2) Simconcept(t1,t2) where, α,β, adopting the Sigmoid function, is used as coordination factor, α = 1 1 +  - Sim editDist  ( t 1 , t 2 ) , β = 1 1 +  - Sim context  ( t 1 , t 2 ) ; if the conceptual matching degree of the two words Simconcept(t1,t2) is greater than the correlation coefficient of domain word δ, then the correlation of domain word is to high, otherwise the correlation of domain word is low; where the correlation coefficient of domain word δ the word variable input by users.