The Cluster Analysis Of Agricultural Machinery Operation Hotspot Based On GPS Trajectory

With the rapid development of the agricultural machinery informatization and the wideapplication of agricultural machinery monitoring equipment, for agricultural machineryequipped with intelligent vehicle terminal, we can easily get its trajectory data at each timepoint. However, with accumulation over a long period, the agricultural machinery operationtrajectory data will appear in mass characteristics. When it accumulates to a certain extent, itwill inevitably reflect certain regularity. Although the current spatial database system couldquickly make basic operations such as input, delete, query and statistics on agriculturalmachinery operation trajectory data, it cannot unearth the relationships and laws hidingbehind these agricultural machinery operation trajectory data, resulting in "rich spatial data,but poor spatial knowledge". Therefore, it becomes more and more important to excavateknowledge from vast amounts of agricultural machinery operation trajectory data automatic-ally.Fristly, the paper gave a systematic description of the current five clustering methodsin the field of data mining, described the classical algorithm and its improved algorithm ofthese methods in detail, and then compared and analyzed the advantages and disadvantagesof the five categories of clustering methods, on this basis the paper proposed a new algotith-m called density-based segmentation clustering algorithm. This algotithm combined thedensity-based and grid-based clustering method, therefore, it has the advantages of density-based clustering method, for example The algorithm is able to identify clusters of arbitraryshape; not sensitive to the order of the input data; can get better clustering result and duringthe density split, combined with grid-based clustering method to solve the problem ofdensity-based clustering algorithm inefficient in the face of large data sets. Secondly, inorder to further improve the efficiency of this article clustering algorithm, by an introductio-n of the more popular spatial index structure by different categories; analyzed their strengthsand weaknesses; then selected the spatial index structure used in this paper, which calledspace partition tree and gave it a systematic and comprehensive elaboration. Then, throughthe clustering algorithm used in this article, the paper realized the automatic recognition andextraction of the agricultural machinery operation hot spot which is high density area. Andto determine the cluster parameters (mesh size and density threshold), according to the scaleeffect, artificially changed the grid size, experiment multiple agricultural machineryoperation trajectory data determined how to divide the grid in order to separate theagricultural machinery transport transfer point in the road from operation point in the field,and then determined the appropriate grid size and density threshold. At last, from thealgorithm efficiency and quality of clustering two classical clustering algorithm is verified by comparing the effectiveness of the proposed clustering algorithm in two aspects of thealgorithm efficiency and quality of clustering, and analyzed the results of clustering;excavate useful information to assess the efficiency of agricultural machinery operations;providing decision support for agricultural scheduling, in order to give agriculturalmachinery operation better guidance.