Study On Fast Forestland Border Data Search Technology

The forestland data reflects the status and changes of forest resources, is an importantbasis for the development of China’s ecological construction management decisions. Thenational forest "a map" system has collected over500TB of national basic geography data,remote sensing data, terrain data, forestland border data and various thematic maps data,which forestland border data has amounted to more than6783million pieces, became thecountry’s largest-ever forestry spatial database. With the deepening of the application andupdate the investigation in full swing, the scale of forestland border data will increase quicklyand become a truly "big data". The face of the increasing scale of spatial data, the limitations ofthe traditional spatial data management methods behaved more and more prominent,and thereis no suitable GIS framework can be used to solve the problem of forestry about massivespatial data. In this context, this paper has done some in-depth studies and researches aboutparallel GIS system on the application of forestland border data query and its key technologies.This paper deeply analyzed the problems existing in the traditional management system,combined with the characteristics of forestland border data, designed a fast query system whichcore was a high-speed parallel GIS about the forestland border data, expounded the keytechnologies, realised a prototype system to verify the correlation technologies. The resultshows that the prototype system has greater advantage compare to the traditional managementsystem,the system can meet the requires about the management of forestland border data incurrent and future, which has the certain popularization and application value.The researchworks this paper have done are following:(1) established a theoretical system about how toquick query the forestland border data, provided three key technologies;(2) The data storagelayout study:solved the problem of data granularity classification through the selection ofadministrative boundary and test the particle size, solved the problem of the discrete layout ofthe data through the graph vertex coloring theory. The test result showed that the smaller the particle size, the faster the data, in the return to full library of about1/3of the big results of thequery test data, by county division at the rate of17052milliseconds, is1.8times of30373milliseconds divided city; By county division of discrete storage layout is clustering layout1.35times;(3)The parallel distribution scheduling study:based on the characteristics of fastquery system, designed a parallel computation model for three layer of correlation. The first isa task allocation and scheduling algorithm according to CPU performance; The second presentsa concurrent and computing power according to the data node allocation and schedulingmethods and data copy; The third layer gives method that using the different stages of thread toa CPU calculation execution needs of allocate different scheduling. Finally during the test,the system with four data node speed reached3.7times that of the traditional managementmode, got close to the linear acceleration ratio;(4) reduction of collection of queryresults:through the establishment of data classification model for automatic data transmissioncontrolto solve the query response speed problem; the main information extraction andspatialattribute data data thinning, reduced the transmission capacity; reduce two related querycostby the master node memory, data memory, three level from main-node cache, data-nodecache DB table. The testresults show that the query returns, edge edge, greatly improves theresponse time, the query response time of less than2seconds in the test.The innovation of this paper are as follows:(1) the data layout is studied from the angle ofapplication of static load balancing, the layout method based on graph vertex coloring theory,realizes the data uniform discrete layout in the server in the cluster, innovative;(2) analysis ofthe impact of cluster structure of parallel computing process, put forward according to themethod of cluster structure parallel computing tasks are hierarchical, associated with thescheduling of multi parallel computing task, improve dynamic computing tasks in cluster loadbalance level.