Wheel conversion method based on big data analysis for box bottom

Abstract

The invention relates to a case bottom wheel conversion method based on big data analysis. The method includes converting a bottom wheel of a cabinet using a bottom wheel conversion system based on big data analysis. The bottom wheel conversion system based on big data analysis includes a wheel switching device for replacing a current wheel of a tie rod cabinet from an overcoming resistance wheelto an anti-skid wheel upon receiving an anti-skid switching command. The wheel switching device is also used for replacing the current wheel of the pull rod case by an anti-skid wheel when receiving the overcoming resistance switching command; an accommodating space arranged at the bottom of the pull rod box for accommodating the resistance wheel or the anti-skid wheel; a contour sharpening devicefor performing the following contour sharpening actions based on the big data analysis; a DSP process chip is used for issuing an overcoming resistance switching command when the fatigue level exceeds the limit, and for issuing an anti-skid switching command when the fatigue level doe not exceed the limit.

Description

technical field [0001] The invention relates to the field of big data, in particular to a method for converting wheels at the bottom of a box based on big data analysis. Background technique [0002] One of the ultimate application fields of big data analysis is predictive analysis, mining characteristics from big data, and establishing scientific models, and then bringing in new data through the model to predict future data. [0003] The diversification of unstructured data brings new challenges to data analysis. We need a set of tools to systematically analyze and refine data. Semantic engines need to be designed to be artificially intelligent enough to actively extract information from data. [0004] Data quality and data management. Big data analysis is inseparable from data quality and data management. High-quality data and effective data management can ensure the authenticity and value of analysis results, whether in academic research or in commercial applications. ...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the technical problem that the trolley case lacks an effective push adaptive control mechanism, the present invention provides a method for switching wheels at the bottom of the case based on big data analysis, and adopts an image recognition mechanism based on big data analysis to reduce fatigue of the trolley case holder Level to conduct on-site detection, so that when the current wheels of the trolley case are too tired, replace the anti-skid wheels with resistance-resistant wheels to increase the pushing speed of the trolley case; among them, based on the fitting results of each target shape in the image, Obtain the module size of the image division, and process each divided image area with different filtering modes based on the signal-to-noise ratio, wherein the first identification processing device is used to process the image more clearly and the second identification processing device is used to process the image The ringing effect is more superior, and when the first logo processing device is selected, the filter order of the Butterworth filtering action performed on the processed image area is inversely proportional to the signal-to-noise ratio of the processed image area, which improves the Efficiency of Image Filtering Computational Volume

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