Design And Implementation Of Intelligent Extraction System For Printing And Dyeing Quotation Requirements

With the introduction and implementation of the "Industry 4.0" concept and the rise of the C2 M Internet business model,It has been an urgent problem of most printing and dyeing enterprise that how to collect and analyze the user's quotation needs in the printing and dyeing industry quickly and accurately.The research on this issue has important practical significance.The quotation of dyeing products is an indispensable step before the order is generated.The structure and standard of the customer's requirements is the premise of the quick and accurate quotation in the marketing process of the printing and dyeing enterprise.The humanized quotation experience and the accurate quotation service are guaranteed the success rate of the order.At present,the quotation way adopted by most printing and dyeing enterprises is still in the form of traditional manual services such as telephone or mail.It not only consumes a lot of working time and inefficiency of manual customer service,but also provides a cumbersome problem for the unified analysis of large-scale user quotation requirements data.In the printing and dyeing industry,there is still no suitable solution for the intelligent extraction service of dyeing quotation requirements.In view of the above problems,This paper analyzes the specific process of quotation before the actual order generation of printing and dyeing enterprises,takes into account the demand factors and the influencing factors in the process of quotation requirements extraction,and finally determines the definition of the problem of extracting printing and dyeing quotation requirements.On the basis of clarifying the definition of the problem,the paper combines the finite automata theory in computational theory to simulate the problem,and establishes the corresponding mathematical model for the requirements collection analysis in the quotation process.At the same time,the paper combines the intelligent robot customer service trained by the Seq2 Seq model in deep learning,optimizing the user quotation service experience and provide an efficient solution to this problem.Finally,the actual order data of Huafang Co.,Ltd.was used for testing,and the efficiency of the actual quotation was verified by the intelligent requirements collection and analysis system.The results showed that the efficiency was significantly improved compared with the traditional manual customer service form.An intelligent extraction system for dyeing and printing quotation requirements was designed and implemented with the above research on the problem.The system uses the JavaScript development language to develop on the Visual Studio Code development tool.It adopts the React Native + Express system framework,using the MongoDB database,and the website is deployed on the Node.js native server.Communication between client and server is achieved through AJAX and Socket.io technology.After the system is implemented,the system has been thoroughly and rigorously tested.The system test results show that the system has high quality and high reliability.