Box bottom material type identifying method

Abstract

The invention relates to a box bottom material type identifying method. A box bottom material type identifying mechanism is started to identity a material type, the box bottom material type identifying mechanism includes a light quantity detection device which is arranged at a bottom end of a box and is used for detecting the light quantity at the bottom end of the box to obtain the light quantityof the corresponding bottom end and outputting the light quantity of the bottom end, an LED illumination device which is connected with the light quantity detection device and is used for automatically emitting the light to realize illumination operation at the bottom end of the box when the light quantity of the bottom end is smaller than or equal to a preset light quantity threshold, a high-definition shooting device which is arranged at the bottom end of the box at one side of the LED illumination device and is used for performing high-definition shooting operation for a material below thebottom end of the box to obtain and output a corresponding high-definition material image, and a big data server which is used for performing identifying processing of an image based on material image characteristics to obtain the material type corresponding to the image taken as the material type below the bottom end of the box.

Description

technical field [0001] The invention relates to the field of big data processing, in particular to a material type identification method 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 [000...

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 method at the bottom of a box. On the basis of big data processing, the bottom of the box can be accurately determined 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

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