Research And Application Of Massive Information Fusion Based On D-S Evidence Theory

With the rapid development of multi-sensor technology, a variety of complexapplications-oriented background sensor system are emerged, information fusion technologyhas been applied in many fields. In these sensor systems, the information is often presentedcomplex, this complexity is mainly reflected in the huge number and diversity forms ofexpression. However, most of the traditional information fusion research is used for simple andsmall amounts of data. With the advent of the massive data era, there are limitations oftraditional information fusion. In this paper, evaluation of environmental comfort as theresearch background, through the mass of information fusion based on evidence theory, andmassive multi-source information conducted experiments to explore, for the efficient andrational use of massive amounts of data to provide theoretical and technical basis. This paperproposed a massive information fusion method based on evidence theory, it can effectivelysolve the information is incomplete, uncertain, large volumes of data and other difficulties.This paper presents a combined mass of information clustering and convex function ofevidence theory fusion method. The main idea of this method is to first collected pretreatmentmass of information by BIRCH clustering algorithm, forming a plurality of clusters. Secondly,the centroid of each cluster is calculated to be the representation of every cluster. Then, to formthe evidence provided by the information in each cluster, the centroid information is given theBasic Probability Assignment based on the generalized triangular fuzzy membership function.Finally, evidences are combined according to the combination rule of the Convex EvidenceTheory. So far, the massive information fusion is completed. In this paper, an indoorenvironmental comfort judging system to verify the effectiveness of the method, the methodcan also be easily extended to other state assessment system.In this paper, indoor environmental comfort as application background, select thetemperature, humidity and PM2.5three important factors, according to a large number of realtemperature and humidity sensor data and number of continuous days PM2.5data collected atdifferent times, the algorithm experimental verification. Since the data of temperature andhumidity sensor data collection more, before the data integration, we need to use BIRCHclustering the data, this paper identifies the node threshold and branching factor according to the characteristics and experimental data, using multiple cluster after cluster instead of theoriginal data, and with representatives of each cluster centroid clusters. Information obtainedafter each cluster, we need to generate the basic probability function, we use the method oftriangular fuzzy membership functions. Since the application of the existence of acorresponding plurality of comfort temperature or humidity range, for which we have the basisof fuzzy membership function based on the method made some changes have been establishedfor each interval of a membership function, and finally chose a large likelihood functioncorresponding to the temperature and humidity, as the likelihood of comfort, and thenprocessed to generate mass value of the evidence. In combination formula convex function ofevidence theory in general, evidence of the impact of different factors are not the same, and wehave the characteristics of the data, the number of design data to the proportion of each clusterin the whole data as a factor this can be quite reasonable to assign weights. Finally, the use ofconvex combination formula fusion. By compare with the direct data fusion algorithm,experiment shows that the proposed algorithm can result in ensuring the correct premiseintegration, and significantly reduce the time of massive information fusion, it is a reasonableand efficient mass of information fusion method. It is provides a new way to explore themassive information fusion technology. Finally, we give the realization of the algorithm basedon hadoop platform.