Product quality alarm method and device based on ensemble learning

Abstract

The embodiment of the invention discloses a product quality alarm method and device based on ensemble learning. A specific embodiment of the method comprises the following steps: detecting whether an operation authorization signal is received from target terminal equipment or not; in response to the detected operation authorization signal, detecting and obtaining an initial product quality sample data set, wherein the initial product quality sample data set comprises an initial product physicochemical index data set, an initial product gas emission index data set and an initial product evaluation result data set; generating a target product quality sample data set; inputting the target product quality sample data set into a predetermined prediction model to obtain a prediction result set; and pushing the prediction result set to the target terminal equipment so as to control the target terminal equipment to carry out alarm related operation. According to the embodiment, the pre-determined prediction model is used for performing quality analysis on the obtained initial product quality sample data set, the accuracy of the quality prediction result can be improved, and the product quality control level can be improved.

Description

technical field [0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to information processing methods and devices. Background technique [0002] The internal quality of cigarettes, such as sensory quality, is closely related to certain physical and chemical components in cigarettes. These physical and chemical components determine the internal quality of cigarettes to a certain extent, such as nicotine in tobacco and sensory stimulation. Feature related. Domain experts often need to refer to these detected components when performing sensory evaluations. Tobacco leaves contain numerous chemical constituents. The interaction of various chemical components in the smoking process stimulates people's senses of taste, smell, and touch, all of which are extremely complex. How to analyze the chemical composition, sensory evaluation and smoke of tobacco leaves is of great significance to reduce the production design cost of en...

AI Technical Summary

Problems solved by technology

Controlling the internal quality of cigarettes in the case of small sample data has the problem of low quality control level, and then there is a risk of quality control failure, which makes false alarms for low-quality cigarette products

[0005] Second, the amount of sample data for cigarette quality control is small, and the accuracy of using a single model to learn small sample data is low, which cannot meet the requirements of cigarette quality control, which in turn affects the triggering of low-quality cigarette alarms

Images