Design And Implement Intelligent Warehouse Cargo Position Managment System Based On IBeacon

With the advancement of intellectualization in various industries in China and the acceleration of digital society construction,warehouse management is an indispensable part of modern logistics industry,and its intelligent management is the development direction of modern warehouse.In the face of the accumulation of goods in the warehouse,timely determination of the location of goods is the basis of intelligent management.As the key technology of business intelligence application,indoor positioning technology helps to improve the efficiency of warehouse operation and reduce the retrieval time of goods manually.This paper analyzes the current development of intelligent warehouse system.Under the premise of ensuring the positioning accuracy of warehouse goods,the iBeacon beacon with low power Bluetooth technology is used to obtain the location information of warehouse goods.The intelligent warehouse goods positioning management system based on iBeacon is designed and implemented.The main work is as follows.:Firstly,through the analysis of the indoor positioning technology based on iBeacon,it is decided to use the location fingerprint algorithm to realize the positioning of warehouse goods.The hardware was deployed in the real warehouse environment,and the fingerprint data at different locations in the warehouse area were collected.Considering that the complex indoor environment composed of many iBeacon beacons and warehouse scenes will affect the accuracy of the offline fingerprint database,Kalman filter is used to filter the collected raw data,and an offline fingerprint database that can accurately reflect the characteristics of each location in the warehouse area is established.Secondly,in order to improve the matching efficiency of the positioning system and the positioning accuracy of the goods,the warehouse area is divided in space.A space partition method based on artificial bee colony algorithm to optimize K-means algorithm is proposed.Using the optimization behavior of ABC and the ability to jump out of local optimum,the optimal clustering center of K-means algorithm is determined to avoid the problem of local optimum when clustering,and the accurate partition of fingerprint space is realized.When positioning and matching the points to be measured at the cluster boundary point,the location information of adjacent subspace may be lost.An adaptive matching algorithm for multiple subspace fingerprint data is proposed,and the number of matching subspaces is adaptively judged according to the threshold.For the K nearest neighbor points selected by the weighted K nearest neighbor algorithm,there may be outliers.A weighted K nearest neighbor positioning algorithm based on local outlier factor is proposed to calculate the local density of the matching K nearest neighbor points,and eliminate outliers that are far away but similar to fingerprint data,so as to obtain the accurate location of goods.Through the comparative experiment and the real warehouse scene cargo positioning experiment,the scheme can analyze the location of goods efficiently in the warehouse scene.Finally,combined with the improved location fingerprint positioning algorithm,the warehouse cargo positioning management system needs analysis and design.Completed the demand analysis of warehouse goods positioning management system,the detailed design of each functional module and the overall design of the database table,and then realized the intelligent warehouse goods positioning management system based on iBeacon.At the same time,according to the requirements of different roles in the warehouse,the mobile APP based on Android and the background management system based on Web are developed,and the functions of iBeacon device monitoring,cargo positioning,cargo access,warehouse cargo management and historical location trajectory playback are provided for warehouse managers.