Research On Cloud Logistics Service Discovery And Composition Method

Cloud logistics service(CLS) platform makes the distributed entity logistics resources and capabilities virtualization and encapsulation with the Internet of things(IOT) and cloud computing technology and stores them, supplies users the application of whole logistics life cycle with the service form of virtual resources by network, integrates CLS resources depending on large-scale cloud computing capacity, flexible business coverage, accurate link control, standard operation process and intelligent decision support system, and matches the best logistics resources for customers’logistics tasks by CLS discovery and composition methods, provides complete logistics solutions to all kinds of logistics enterprises, logistics hub and big business departments. At present, the researches on cloud logistics have made some valuable results, but there are few in-depth researches on CLS because most of them make research on cloud logistics platform building, virtual perception technology, and so on. Traditional service management cannot meet the complicating and personalization logistics demand with the growing and changing of CLS’number, type, content, etc. It is eagerly to study CLS formal description, discovery and composition by using IOT and cloud computing technology from the perspective of improving meeting demand and efficiency.The paper in-depth researched the business process, operation mechanism, formal description, discovery and composition methods of CLS. For CLS business process and operation mechanism, defined the concepts of cloud logistics and CLS, analyzed the business process of CLS, built the architecture of CLS, studied the operation mechanism from the aspects of resources as service, cloud logistics task decomposition, CLS discovery and composition and CLS collaboration; For CLS formal description, analyzed the matching relationship between CLS and cloud logistics task, built CLS ontology model and CLS ontology, researched CLS formal description method based on context and proposed context reasoning algorithm based on OWL; For CLS discovery, designed CLS discovery process, studied CLS discovery algorithm, and proposed improving matching algorithm based on distributed K-Means clustering algorithm from the angle of adaptability and efficiency; For CLS composition optimization, analyzed CLS composition process, studied CLS composition methods from classification module, negotiation module and optimizing module, proposed solving method and algorithm of multi-objective composition optimization problem based on cloud differential evolution algorithm. Main innovations of the paper are as follows:1. CLS formal description method was proposed based on contextThe exiting formal description methods mostly research on Web service and cloud services of particular occasion, while CLS cloud logistics resources modeling has unique characteristics in CLS model, which are all taken into account in the paper. The paper proposed CLS formal description method based on context and built cloud logistics domain context ontology, which was used to descript concepts and logic relations, promoted CLS formalization research in cloud environment, which laid CLS basis of distributed storage and computing.2. CLS discovery method was proposed based on distributed K-Means clustering improving matching algorithm.The exiting service discovery methods are most confiened to a particular situation and have larger dependence, which cannot be transplanted in CLS platform. For the discovery demand of CLS in cloud logistics systems, the paper analyzed CLS discovery algorithm, which integrated semantic parsing, ontology reasoning, functional matching and non-functional matching algorithm. Then from the perspectives of adaptability and efficiency, the paper improved the matching algorithm based on distributed K-Means clustering algorithm, which improved the efficiency, recall rate and precision of CLS discovery and provided a theoretical basis for future study in CLS.3. CLS compositon method was proposed based on cloud differential evolution algorithm.The exiting service composition methods are most lack of targeted and systematic. Combining with the multiple attribute and constraints features of CLS composition process, the paper analyzed CLS composition method from classification module, negotiation module and selection module, proposed multi-objective composition optimization algorithm based on cloud differential evolution algorithm, which promoted the research on processing large amount of data, improved composition efficiency and accuracy and provided a certain guiding role for providing complete logistics solutions and developing cloud logistics.