Research On Data Management Method Of Undersea Environment Detection With MongoDB

The in-situ environment of the ocean floor includes various aspects such as electrochemical,biological,vibration,pressure,depth of salt temperature,and nuclear radiation.The in-situ environment detection data as the basis of marine data management analysis is of great significance to the marine geophysical field.Through the analysis of the seabed collected data by the seabed in situ environmental detection platform,we can know the structural information and dynamic changes of the seabed space,including changes in physical information units,chemical characteristic elements,and biological system structures.In recent years,with the continuous development of acquisition technology and equipment,the structure of subsea detection data has become more and more complex,and the amount of data has gradually increased,which has brought huge challenges to the management of subsea data.Therefore,it is of great research significance to manage the massive and complex subsea detection data efficiently and develop a set of methods suitable for subsea detection data management.This article introduces a method for realizing submarine in-situ environment detection data management based on MongoDB distributed database,and provides effective and fast support for subsea detection data processing platform software.First of all,this thesis analyzes the characteristics of several types of non-relational databases that are relatively common at present,and puts forward the advantages of MongoDB,a non-relational database,for seabed exploration data storage.Aiming at the characteristics of multi-source heterogeneity of seabed detection data,combined with client software and user requirements,the storage model of seabed detection data was analyzed,and a flexible database structure model was designed based on MongoDB.Then,a detailed development and design of the management method of submarine detection attribute data and large binary data is carried out.The BSON data format is used to store submarine detection attribute data,and the GridFS distributed file system stores large submarine detection binary data.According to business needs,using the QT integrated development environment in conjunction with the MongoDB C ++ driver and JSON library to develop a number of API function interfaces,can quickly and effectively implement the communication between the database server and client software.Aiming at the design of the data management platform performance,this article sets up a replica set on the MongoDB database server to implement the load balancing feature.It uses sharding technology to deploy the MongoDB cluster to achieve horizontal expansion.The physical storage design uses disk array RAID10 as the physical storage method to solve mass data storage.Redundancy and increased security of data storage.Finally,this thesis uses the established platform system to perform functional testing and verification analysis on different types of subsea collected data.Through internal testing,it is concluded that the management method designed for the in situ environmental detection data of the seabed designed in this paper has stable performance and can effectively complete the functions of storage,query,and retrieval of multi-source heterogeneous seabed detection data,and the effect has reached the expected design goal.And got good feedback.