Research On Management Of Massive Data In Workshop On-site Manufacture "Local Internet Of Things"

The level of on-site management of the product manufacture plant largely on behalf of theproduction levels of the manufacturing enterprises. Now a days, the demand of data management inworkshop raises as the manufacturing technique of products gets more complicated, the raw materialsfor production gets more variegated and the requirement of secrecy gets higher so it is urged thaton-site data management platform which meets the demand above to be researched and developed. Sothat in this thesis a massive data management platform on “Local Internet of Things” of workshop isresearched to raise the on-site management level, to increase the workshop production capacity and toimprove production efficiency through managing the data in the workshop in the process ofproduction.Firstly, the current situation of the research on on-site data management is analyzed and a systemframework is raised with combination of the technology of emerging Internet of Things&massivedata processing. The specific structural and functional of each layer, including data acquisition layer,data service layer, business logic layer and the user interface layer, and the main flow in theframework of the system is analyzed.Secondly, the key technologies of the system are elaborated, including the real-time dataacquisition and access in “Local Internet of Things” and the data storage and retrieval in distributedenvironment. In this thesis, universal data acquisition rule and policy based on that on-site data isgathered and parsed is established and the collected data is cached in the data cache, then the elementsin data cache, including data pool, thread pool, data rule set are maintained and data is exchangedusing the way based on Windows Message mechanism to support user’s real-time data requirement.After that, Data storage format in distributed file system is defined and massive data is stored intohdfs using the sub-block algorithm based on improved RS codes. And then the specific process of dataretrieval on improved hdfs with MapReduce framework is elaborated.At last, the background of the project and the implementation of the core technology areelaborated. The implementation is applied in the project and good performance is demonstrated.