Java-based Workshop Remote Monitoring System

Intelligent manufacturing is the focus of the "Made in China 2025”program.The data management and monitoring system in smart factories is an important aspect of intelligent workshop construction.The main functions of the Java-based workshop data analysis and monitoring system are the display of the configuration information of the workshop parts and mechanical equipment,the display of the dimensions of the machined parts and their error,the display of health data of the workshop equipment,the calculation of the eigenvalues,the display of the 3D model of the workshop,etc.It solves the data management problem in the intelligent workshop production process,establishes the data analysis and monitoring system,realizes the intelligentization of the workshop production through the data analysis technology,comprehensively improves the product quality and enhances the management ability of the enterprise.System optimization is an important method to improve the real-time performance of the system.In the optimization of the transmission model,the host computer system abandons the establishment of the traditional table object Entity,uses the HashMap structure,directly stores the query data into the structure,reduces the mapping process of data to the table object,reduces the data type of the database and the Java Conversion between basic types to facilitate data transfer The use of generics in the lower computer system simplifies the amount of code and guarantees versatility and efficiency.Analyze the rationality of system modules by building complex network models and improve the robustness of the system by finding key nodes.According to the characteristics of the system data query,the index is established on the columns of the frequently queryed column,the column indicating the state,the column of saving time,and the column in the multi-table join query,wherein the index established on the column with the smaller value type is Bitmap index.The ant colony algorithm used in query the application model to calculate the optimal query path for multi-table joins.Querying,analyzing and visualizing the workshop data,first configure the self-starting thread,then perform statistical analysis on the data queried by the self-starting thread,calculate the eigenvalues,and finally use the charts and the 3D models to visualize the feature data.