Research On IoT Data Visualization System Of Electric Forklift's Working Condition

Electric forklifts play an important role in many fields related to national economy and people's livelihood.With the wide application of Internet of Things technology,communication technology and smart sensor technology on electric forklifts,The data of IoT working conditions characterized by a large variety,large quantity and strong timeliness has grown rapidly.However,traditional spreadsheet management methods are difficult to discover the intrinsic link of these data and cannot fully utilize the potential value of IoT data.Therefore,it is a valuable subject to study the efficient presentation of IOT data information of electric forklift working conditions and provide management decision-making basis for managers.This paper takes the electric forklift in the warehouse logistics workshop as the research object,and designs and implements the I/O data visualization system of the electric forklift working condition based on B/S structure.Firstly,the paper expounds the theory and technology of visualization.By analyzing the characteristics of visual charts,appropriate charts are selected and applied to the system.Secondly,through in-depth analysis of the actual functional requirements of the system,the overall architecture of the system is designed,and the detailed functional modules of the visualization platform and the selection of related technologies,languages and visualization tools are determined.Finally,a graphical visualization system was developed using Echarts+HTML+CSS+Bootstrap+PHP+ MySQL.Based on the cloud server,the system realizes the statistical chart display of real-time monitoring,operation monitoring,fleet efficiency analysis,fault management,vehicle and personnel information of electric forklift.After testing,the user interaction of the system is good and the security is high.This paper deals with the inefficient maintenance mode of maintenance after the electric forklift fails.Combined with the working condition of the electric forklift,the gray system GM(1,1)and BP neural network prediction are combined to introduce the vehicle with gray neural network.The fault prediction model predicts the vehicle failure trend and makes timely maintenance and maintenance decisions to achieve vehicle maintenance based on prevention.The system can effectively help the warehousing and logistics management personnel to view the obscure data in the original database and accurately grasp the current situation of the vehicle.Through the analysis of vehicle speed,battery,workload,faults and other data,we can understand the driver's driving habits,indirectly form an assessment of the personnel,and realize the safety integration of the management,management and management.In general,the system is a summary analysis chart for enterprise vehicles,personnel,etc.,which provides a powerful basis for decision-making,evaluation,and has certain practical value.The system can effectively help warehouse logistics managers to view the vast amount of data in the original database and accurately grasp the current status of the vehicle.Through the analysis of vehicle speed,battery,workload,and faults,the driver's driving habits can be understood,and the assessment of personnel can be indirectly formed,thereby achieving integrated management of people,vehicles and safety.In general,the system provides a summary chart for the analysis of various types of information on enterprise vehicles and personnel,providing a powerful basis for decision-making and evaluation,and has certain engineering practical value.