Method and system for establishing product process quality prediction model based on site data

Abstract

The invention discloses a method and a system for establishing a product process quality prediction model. The method comprises the following steps of: acquiring site data of cigarette equipment and storing the acquired data to a database; processing the site data of the cigarette equipment, wherein the site data of the cigarette equipment include shutdown data, deletion data, yield data and product quality data of a cigarette making machine; establishing a product process quality neural network prediction model; establishing an equipment fault tree; inputting real-time data of the cigarette equipment to the product process quality neural network prediction model to obtain prediction product quality data, and controlling the cigarette equipment in combination with the equipment fault tree. According to the method and the system for establishing the product process quality prediction model based on the site data, the singleness of a data statistical analysis method can be avoided; the data of the cigarette equipment are effectively combined, a data comprehensive analysis model is established, the product quality in the next period of time is predicted, defective products are prevented from being produced, the product quality-oriented equipment management is guided.

Description

technical field [0001] The invention relates to the field of establishing data models in the tobacco industry, in particular to a method and system for establishing a product process quality prediction model based on field data. Background technique [0002] In recent years, the industrial automation of cigarette enterprises has made considerable progress, and systems such as bottom-level data acquisition, centralized control, and status monitoring have been established. The management has also established a large number of application systems. There are a large number of industrial automation systems and application systems in these Equipment data resources, but the level of data management and data value mining in equipment management is not high, resulting in the waste of a large amount of data resources, which cannot effectively support lean management. [0003] On-site data of cigarette equipment mainly includes downtime data, reject data, output data and product qualit...

AI Technical Summary

Problems solved by technology

[0004] At present, the prediction of the product process quality of cigarette equipment is only based on manual judgment based on the change trend of cigarette quality and experience, which is relatively subjective, without analysis based on relevant data, and the accuracy is not high

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