算法源代码工程

说明：  对于通过实验对叶片的各微量元素的质量提取分析其成分得到其植被带信息，通过我们经过仪器检测得到的碳氮硫等元素的含量值以及植被带类别，对于新型物种，经过对大量数据采集分析，那么存在的问题可能会是对功效成分及含量标准认识不清，或者只通过某一指标而忽视其他指标。这样看来，通过KNN算法，在我们已知的带有标签和含量测试的大数据条件下，能够比较快地而且准确地获取判断出其植被带信息，从而提高工作效率和识别准确度。
(For the quality extraction and analysis of Microelements in leaves through experiments, the information of vegetation zone can be obtained by their components, the content value of carbon, nitrogen, sulfur and other elements as well as the types of vegetation zone can be obtained through the instrument detection, and for new species, after a large number of data collection and analysis, the existing problems may be unclear understanding of the functional components and content standards, or only through a certain One indicator and ignore other indicators. In this way, through KNN algorithm, under the condition of big data with label and content test that we know, we can get and judge the vegetation zone information quickly and accurately, so as to improve the work efficiency and recognition accuracy.)