A printing and dyeing quotation structured demand data extraction method based on a finite state automaton

Abstract

The invention relates to a printing and dyeing quotation structured demand data extraction method based on a finite state automaton. The method is characterized in that a formalized definition of theuncertain finite-state automaton is given according to an actual quotation process and requirements, a real quotation process is simulated through the uncertain finite-state automaton, a user is gradually guided to give an accurate requirement, and structured data is arranged and provided for a customer service. Compared with the prior art, the printing and dyeing quotation structured demand dataextraction method based on the finite state automaton provided by the invention has the beneficial effects that the method does not depend on manual collection and arrangement demands, and the efficiency is remarkably improved. The method is short in processing period and has reusability; the method is low in processing cost and high in accuracy.

Description

technical field [0001] The invention relates to a method for extracting structured data from user input requirements in the quotation function of the dyeing and finishing industry, which belongs to the dyeing and finishing industry. Background technique [0002] With the introduction of concepts such as Industry 4.0 and mass customization, more and more enterprises have begun to transform to the C2M (Customer-to-Manufactory) model. C2M is an industrial customization model that connects different production lines together through the Internet, sets suppliers and production processes according to customer product order requirements, and finally produces personalized products. The textile dyeing and finishing industry is a traditional industry with relatively high labor intensity in my country. With the continuous improvement of the demand for refinement and intelligence in the production management process, the traditional ERP management platform has been unable to meet daily ...

AI Technical Summary

Problems solved by technology

[0004] 1. Contacting customer service by phone takes up a lot of time, and one customer service can only serve one user at a time, resulting in low customer service efficiency

[0005] 2. Contact customer service via email, which has high communication costs and poor immediacy. Frequent confirmation is often due to ambiguity in user needs, and a large number of quotation requirements are accumulated. In addition, users cannot get timely feedback, and the experience is poor.

[0006] 3. The requirement descriptions provided by users are often in natural language, and there are situations where the requirements are vague. Customer service needs to understand and manually sort them into structured requirements, which takes a long time, has low accuracy, and has no reusability

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