Method for improving product qualification rate based on multi-dimensional decision tree group

Abstract

The invention discloses a method for improving the product qualification rate based on a multi-dimensional decision tree group. The method is characterized by taking a production factor as a classification standard to construct different decision trees so as to form a multi-dimensional decision tree group; providing multi-dimensional optimization suggestions, including highest qualification rate configuration suggestions; determining the influence factors which have great influence on the product qualification rate can according to the influence degree of the influence factors; performing auxiliary decision making, so as to improve the product qualification rate, reduce correction, and improving work efficiency improvement; or directly giving the suggestions that the configuration is changed at least under the condition of higher qualification rate. The method supports real-time data updating and dynamic growth of the decision trees; basic working conditions can be finely adjusted through machine learning, so that the productivity change after fine adjustment can be learned; and at the same time, due to the fact that the number of equipment parameters is large, the decision tree group needs large space while the decision tree group and the machine learning module can be switched in different memory spaces, and needed data can be flexibly dispatched or learned in the corresponding memory space.

Description

technical field [0001] The invention relates to the technical field of production management, in particular to a method for improving product pass rate based on a multidimensional decision tree group. Background technique [0002] Improving the product qualification rate of industrial production lines is an important topic of concern in the industry. The main difficulty lies in the inability to accurately control the parameters of each process machine in the production line to maximize the product qualification rate. [0003] At present, machine control in the industrial field is basically operated according to manual experience, and there are too many machine parameters, and it is impossible for humans to confirm which machine parameters have a great impact on the pass rate, resulting in the inability to predict the impact of the current operation on the product pass rate, or the machine’s The control needs to be carried out by operators with certain experience, and differe...

AI Technical Summary

Problems solved by technology

[0002] Improving the pass rate of industrial production line products is an important topic of concern in the industry. The main difficulty lies in the inability to accurately control the parameters of each process machine in the production line to maximize the pass rate of products.

[0003] At present, machine control in the industrial field is basically operated according to manual experience, and there are too many machine parameters, and it is impossible for humans to confirm which machine parameters have a great impact on the pass rate, resulting in the inability to predict the impact of the current operation on the product pass rate, or the machine. The control needs to be carried out by operators with certain experience, and different operators will have different operation methods, which cannot be unified, resulting in unstable product qualification rate