Research On Data Management And Analysis System Of Warp Knitting Workshop Based On Internet

The warp knitting industry is an important part of the knitting field,and intelligent manufacturing is a key research project in China ’s textile industry.Internet technology has been gradually applied to most warp knitting enterprises,but most enterprises ignore the management and analysis of machine data in warp knitting workshop,lose the timeliness of data,and fail to effectively and scientifically pre-process the collected data in warp knitting workshop.Aiming at this problem,based on the Internet and cloud database technology,the system presents the warp knitting workshop data through the Web interface in real time,and uses the decision tree algorithm to deeply mine and analyze the data.Finally,the real-time monitoring of workshop data and the pre-allocation decision of production factors are achieved,and the operation efficiency of workshop machines and the production capacity of enterprises are improved.Firstly,focusing on the current situation of warp knitting workshop data management,pointing out the existing problems in the current data management process,according to the current situation,clarifying the functional requirements of warp knitting enterprises for workshop data management system,and establishing the goals and principles of system design,chose the B/S architecture as the main development architecture of the system,discussed the overall framework of the system in detail,and laid a theoretical foundation for the in-depth study of the follow-up system.Secondly,the hardware equipment and technology used for data collection in the warp knitting workshop are determined,including sensors and RFID radio frequency identification technology.After successfully collecting the data,start designing and building the SQL Server database.Through the E-R diagram model,the connection between the collected data is determined,and the data type,primary key and other factors are designed for each data in the data table to create a data information table.Once the database has been set up and the data information table has been crafted,primary and foreign key restrictions,Default regulations,data integrity regulations,etc.are added to the data table to ensure the integrity and security of the data and improve the reliability and maintainability of the database.After the local SQL Server database is established,cross-link the database with the Web application module in Visual Studio.After successfully connecting the data source,the data in the database can be presented on the Web page,which is convenient for the manager to monitor the warp knitting workshop in real time and decision management.Thirdly,based on the cloud database technology,an appropriate configuration was selected to build the Alibaba Cloud ECS server,and the data structure table of the Alibaba Cloud RDS database was designed based on the Alibaba Cloud server.ADO communication technology and FTP communication protocol are used to build an FTP data transmission platform.After testing,the data transmission platform is stable and safe,and the cloud database can smoothly perform data transmission and interaction with the local database.By building a cloud database,it saves the cost of purchasing local computer hardware,and saves a lot of manpower and energy to manage local computer hardware,and because of the flexibility of its data,the cloud database can query and manage workshop data information on the mobile terminal anytime,anywhere,for decision-making The author provides more convenient and intuitive data statistics,and also lays the foundation of experimental data for in-depth mining and analysis of warp knitting workshop data.Finally,based on the C5.0 decision tree algorithm,the data in the cloud database is scientifically modeled and analyzed,and the Boosting algorithm is used to optimize iterations,and error analysis and pruning branches are used to establish a decision tree for machine operation efficiency management in the warp knitting workshop.The impact of various key factors,such as machine model,running time,raw material type,indoor temperature,relative humidity,shift,and operator number,on the operating efficiency of the machine has been excavated.At present,the average operating efficiency of the workshop machines is about96.58%.After the research of the decision tree algorithm,the rational allocation of production factors and personnel input can increase the machine operating efficiency to about 98.5%,so as to truly improve the operating efficiency of the workshop machines.The company’s productivity goals.This paper investigates a data management and analysis system based on the Internet and decision tree algorithm for warp knitting workshops,which utilizes a combination of database,cloud database,and other technologies to analyze data with the C5.0 decision tree algorithm.Real-time monitoring and management of warp knitting workshop data is achieved.Scientific modeling and data analysis also provide reliable and effective planning for rationally arranging the operation of workshop machines,and better improve the efficiency of operation.