RESEARCH ON SAMPLING INSPECTION OF NATURAL RESOURCES IN THE BIG DATA ERA

Abstract. Technologies such as satellite remote sensing, global positioning, laser scanning, airborne radar, tilt photography, and drones are rapidly developing. Spatial data is exploding at an unprecedented rate every day, in a short period of time. The elements of natural resources products vary, and sampling inspection is generally used to evaluate the quality level of a lot of products. The sampling inspection is based on the rigorous probability theory. Samples are randomly taken from a lot of products according to the sampling scheme for inspection, and the quality of the lot is represented by the quality status of a small number of samples. In the small data era, it can achieve quality inspection of natural resources products with the least labor cost and the smallest number of samples. However, how to select a good sample is a difficult problem. In theory, using any set of sample data, we can not get the exact total truth value, and the sampling error is inevitable.

This paper gives an overview of the basic rules of sampling inspection, including the basics of mathematics, basic principles and selection of sampling schemes. It introduces in detail the parameters, characteristics and methods of a sampling inspection, and uses the surveying and sampling scheme adopted for Natural Resources Products inspection. For example, it analyzes the incompatibility of the risk of the producer, the user, the inspector and the sample size selection, and puts forward suggestions for improving the sampling scheme of natural resources products.