Group quantity diversity estimation method based on information moment

Abstract

The invention discloses a group quantity diversity estimation method based on an information moment, belonging to the technical field of internet-of-things data analysis. According to the invention, arecursive sampling process is combined to optimize a sampling process more reasonably; characteristics like diversity in a large group can be greatly reflected by utilizing a population information moment; and an important effect is exerted on group decision and observation of group characteristics. The group quantity diversity estimation method specifically comprises recursive sampling and common group identification, wherein the step of recursive sampling is to stratify the sampling process to obtain a sampling probability; each layer of sampled groups is scanned; a byte bitmap composed oftime slots is given according to a hash function; threshold-based common group identification is performed after sampling; common groups and non-common groups in one ALOHA time slot are identified; and moment estimation of all labels, namely quantity characteristics of all labels are obtained by calculation.

Description

technical field [0001] The invention belongs to the technical field of data analysis of the Internet of Things, and in particular relates to a method for estimating the diversity of group quantities based on information moments, which can be used for the management of item types in large-scale distribution centers. Background technique [0002] With the development of Internet of Things technology, RFID technology has been widely used in warehouse management. The traditional management system is based on the statistics of product quantity and product category, but the pure product quantity cannot reflect the characteristics of this population; The estimation of the size of the species requires the reader to count the number of a group and then power off to activate the tags of the next group to estimate the size of the species. When there are too many types, it will cause a great time loss; if a specific group is sampled, it will If the number of a small number of groups is ...

AI Technical Summary

Problems solved by technology

[0002] With the development of the Internet of Things technology, RFID technology has been widely used in warehouse management. The traditional management system is based on the statistics of product quantity and product category, but the pure product quantity cannot reflect the characteristics of this population; The estimation of the size of the species requires the reader to count the number of a certain group and then power off to activate the tags of the next group to estimate the size of the species. When there are too many types, it will cause a huge time loss; if a specific group is sampled If the number of a small number of groups is huge and whether they are sampled or not during sampling, it will have a great impact on the sampling results, resulting in inaccurate estimation of the group. In addition, the simple number of groups cannot reflect the characteristics of the overall sample well. Therefore, It is extremely important to design a reasonable method of group number estimation and an algorithm that reflects the digital characteristics of the group

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