Research On Monitoring Data Management System Of Automated Stereoscopic Warehouse

Automated three-dimensional warehouses have gradually developed into the core of modern logistics technology with their strong storage capacity and extremely high space utilization.The increasing scale of automated three-dimensional warehouses is accompanied by a large amount of automated three-dimensional warehouse data.In order to improve the interactivity and effectiveness of the automated three-dimensional warehouse monitoring system,the thesis combines virtual technology and data management technology to visually reflect the delivery and storage of goods in the virtual scene through the addition and deletion of the threedimensional model of the goods.At the same time,the user can pass Cargo model view the actual image of the goods on the current shelf.In order to improve the response speed of the system,the optimization of the three-dimensional model and the compression of image data have been completed.At the same time,the functions of user management,early warning management,and data query have been realized.The main work is as follows:1)Analyze the requirements of the system from two aspects of user and system function realization,and demonstrate the necessity of the system to construct virtual scenes and study carton image compression.Analyze and research the functions of the system data management part and the virtual scene part respectively,and complete the overall scheme design of the system;2)According to the actual environment of the warehouse,in 3Ds MAX,three-dimensional models of warehouse goods,shelves,stackers,etc.are constructed in 3Ds MAX through polygonal modeling,and at the same time,real images are used to map the warehouse to complete the construction of the entire virtual scene.Then combine Polygon Cruncher tool and LOD technology to optimize the entire virtual scene model.Finally,human-computer interaction functions such as virtual scene 3D model manipulation,virtual warehouse roaming and virtual warehouse monitoring data viewing are realized.3)Use the K-SVD algorithm to learn the carton image training set,generate a complete dictionary,and then divide the compressed image into blocks.The improved Canny edge detection detects the edge of the image from 45°,135°,horizontal and vertical directions,and calculates each The edge contour area of each image block,according to the proportion of the edge detection contour area in the image block and the overall image edge detection average contour area to adaptively set the error,and at the same time obtain the sparse coefficient matrix after the sparse representation of the image under the original error,According to the comparison between the adaptive error and the original error,the secondary optimization method of the sparse matrix is determined,and the optimized sparse coefficient matrix is obtained,and the high-power compression of the carton image is completed.4)Explains the design and implementation of data storage and transmission schemes,system functions and implementation processes.The algorithm designed in this paper is verified in the system,and the data analysis of the image compression effect is carried out.Finally,the performance and function of the system are tested and analyzed.Through the display,compression and storage design of the monitoring data,a set of automated three-dimensional warehouse monitoring data management system has been completed.Comprehensive tests have verified the effectiveness of the system for warehouse monitoring data management.The designed solution solves the large amount of automated three-dimensional warehouse monitoring data.Convenient management and other issues provide a certain reference value for subsequent automated three-dimensional warehouse data management.