Brief introduction of collaborative artificial intelligence system for packaging steel strip production load distribution

Brief introduction of collaborative artificial intelligence system for packaging steel strip production load distribution
　　 In order to make full use of the valuable information resources on the production site, first establish a data collection system for the production site of the rolling mill and a historical database of strip steel.
　　Using the two-level fuzzy comprehensive evaluation model to analyze the data in the massive historical database of strip steel, it provides a new standard for evaluating the quality of strip steel products and makes the overall evaluation of strip product quality more reasonable.
　　 introduces the artificial neural network method to predict the load distribution of the hot strip finishing mill, and the prediction result of the network can improve the quality of the load distribution.
　　 The hybrid learning system combined with genetic algorithm and BP algorithm provides a brand-new method for solving the structural parameter design of BP network, so that the legacy can be
The global optimization capability of the 　　biography algorithm is combined with the guiding search idea of u200bu200bthe BP algorithm.
　　 Establish an intelligent load distribution expert system. The application of the expert system makes it possible to solve problems that cannot be solved by theoretical modeling and unstructured.
　　The intelligent load distribution system is used to set the load distribution of typical hot rolling products (3.0mmx1300mm) and verify the rolling force of each stand. The results show that the setting is more reasonable. The intelligent load distribution system established by the above method is used to set all slabs in each rolling process in actual production. The results show that the set results can reproduce good rolling conditions, and the set value is better than the load used in actual production. Assign value.