Agricultural Transfer Labor Wage Platform Based on Image Content Feature Retrieval and Internet of Things

Abstract

Quick and accurate information identification of agricultural transfer labor wage platform is an essential function of labor intelligent management in the new era. Based on the content feature retrieval, this study constructs an artificial intelligence identity information recognition system and links the system to the salary platform. Simultaneously, this study uses the feature recognition to extract database information and realize intelligent salary assessment. In addition, the deep learning features used in this study are based on the positional information of the sift features and are finally calculated by the activation map to obtain a global vector of an image. Finally, this study design testing experiment to verify the performance of the algorithm. Through the output of the feature picture, it can be seen that the research algorithm has certain effects and can be used as a follow-up system practice.

Salary scale and the diversity of wage systems

“…Most large companies -although in terms of their method of time studies and detailed cost analysis as they are known in the United States are very lacking -have provided a baseline figure for "efficiency", which is usually called the standard output. If production exceeds this standard figure, the factory is said to be operating in excess of standard efficiency, and from this estimate the basis for providing productivity benefits to workers is provided (Wu & Liu, 2021). An examination of the payroll records shows that production at large firms usually exceeds standard yield rates.…”