Logistics transfer robot work abnormity detection method and system based on image processing

Abstract

The invention relates to the field of image data processing, in particular to a logistics transfer robot work anomaly detection method and system based on image processing. Obtaining video image data corresponding to the logistics transfer robot, wherein the video image data comprises a plurality of frames of video images; extracting each connected domain corresponding to each frame of video image, and calculating a representative value of each connected domain; on the basis of the representative values, matching the connected domains on the two adjacent frames of video images to obtain a plurality of matching pairs; calculating an image entropy of a difference image of every two adjacent frames of video images according to the video image data, and dividing the video image data into a plurality of categories according to the image entropy; based on the matching pairs, discrete coefficients corresponding to various video images are calculated; calculating the maximum similarity matching distance corresponding to each type of video image, and further obtaining the frame transition stability corresponding to each type of video image; and according to the frame transition stability, it is judged that the logistics transfer robot is abnormal. Whether the logistics transfer robot is abnormal or not can be accurately judged.

Description

technical field [0001] The invention relates to the field of image data processing, in particular to a method and system for detecting abnormal operation of logistics transfer robots based on image processing. Background technique [0002] With the rapid development of e-commerce, the emergence of logistics transfer robots has replaced the traditional manual handling of goods, effectively reducing the labor intensity of workers and improving operational efficiency; however, due to the complexity of the working area environment, there may be Adhesive fine particles, screws and other substances, if these substances adhere to the wheels of the logistics transfer robot, after a period of time, these substances will cause abnormal conditions such as slipping, blocking, and blocking of the wheels; abnormalities of the wheels (Slipping, blocking, blocking) will cause the goods being transported to overturn on the ground, which will not only affect the work efficiency but also cause...

AI Technical Summary

Problems solved by technology

[0002] With the rapid development of e-commerce, the emergence of logistics transfer robots has replaced the traditional manual handling of goods, effectively reducing the labor intensity of workers and improving operational efficiency; however, due to the complexity of the working area environment, there may be Adhesive fine particles, screws and other substances, if these substances adhere to the wheels of the logistics transfer robot, after a period of time, these substances will cause abnormal conditions such as slipping, blocking, and blocking of the wheels; abnormalities of the wheels (Slipping, blocking, blocking) will cause the goods being transported to overturn on the ground, which will not only affect the work efficiency but also cause certain property losses; for example: when the logistics transfer robot transports heavy items, if the wheels slip, the logistics The transfer robot will not stop in time due to inertia and cause great losses; therefore, it is very necessary to detect whether the logistics transfer robot is abnormal in time and quickly, so as to avoid unnecessary losses

[0003] The existing detection method is to judge whether the robot is abnormal by using the uniformity of speed through the detection of different frames of video images, but the actual working environment of the robot is extremely complicated, and it is impossible to accurately detect whether the logistics transfer robot is abnormal by only using the uniformity of speed

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