Material type identification mechanism at the bottom of the box

Abstract

The invention relates to a box bottom end material type identification mechanism. The box bottom end material type identification mechanism comprises light quantity detection equipment, LED lighting equipment, high-definition shooting equipment and a big data server, wherein the light quantity detection equipment is arranged at the bottom end of a box, and is used for detecting the light quantityat the bottom end position of the box so as to obtain a corresponding bottom end light quantity value, and to output the bottom end light quantity value; the LED lighting equipment is connected with the light quantity detection equipment, and is used for automatic lighting to realize an illumination operation for the position of the bottom end of the box when the light quantity value at the bottomend is smaller than or equal to a preset light quantity threshold value; the high-definition shooting equipment is arranged at the bottom end of the box and located on one side of the LED lighting equipment, and is used for carrying out high-definition shooting operation on the material below the bottom end of the box, so as to obtain and output a corresponding high-definition material image; andthe big data server is used for carrying out identification processing on the image on the basis of the characteristic of the material image so as to obtain the type of the material corresponding tothe image to be served as the material type below the bottom end of the box. By means of the mechanism, the box pushing power assisting force can be effectively supplied.

Description

technical field [0001] The invention relates to the field of big data processing, in particular to a material type identification mechanism at the bottom of a box. Background technique [0002] The data mining of big data is different from the previous statistical and analysis process in that data mining generally does not have any pre-set themes, and mainly performs calculations based on various algorithms on existing data to play a role in prediction. Effect, so as to achieve some high-level data analysis needs. [0003] Typical algorithms include Kmeans for clustering, SVM for statistical learning, and NaiveBayes for classification. The main tools used are Mahout of Hadoop, etc. The characteristics and challenges of this process are mainly that the algorithm used for mining is very complex, and the amount of data and calculation involved in the calculation are large, and the commonly used data mining algorithms are mainly single-threaded. Contents of the invention [...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the current technical problem of difficulty in pushing boxes, the present invention provides a material type identification mechanism at the bottom of boxes. On the basis of big data processing, it can accurately determine the bottom of the box through a targeted image processing mechanism and image recognition mechanism. The type of material below, and based on the type of material below the bottom of the box, further determine the corresponding booster size, so as to realize the adaptive booster action on the box; set the image to be processed based on the resolution of the image to be processed For the central image block of the image to be processed, determine the number of image regions in the image to be processed as the number of central objects, and formulate different images based on the ratio of the number of central objects to the number of all objects in the image to be processed Filtering strategy improves the cost performance of image data processing

Images