Design And Implementation Of A Cloud-based Unified Identity Intelligent Information Management System

With the development of the information age,people's lives are moving more and more in a convenient,fast and safe direction.Based on the rapid development of artificial intelligence,more and more biometric technologies have been developed.From traditional keys and password locks to advanced biometric recognition technologies,such as face recognition,fingerprint recognition,finger vein recognition,iris recognition,etc.These advanced biometric technologies have their unique advantages: they will not be forgotten or lost,safer,and more convenient.However,as the audience increases,the amount of data will become larger and larger,so how to better manage the complex data,more efficiently handle these unstructured data,and quickly find the identification data are issues that need to be considered now.To this end,this thesis adopts the solution of building an intelligent authentication information management system with cloud computing Docker container technology from three aspects of data information processing,intelligent identification and authentication and improvement of retrieval efficiency to deal with the above problems,and implements the system according to the actual engineering steps.Development and testing.The main work of this thesis is as follows:In terms of data information processing,a smart authentication information management system has been developed.Since the current system mainly uses face recognition,in order to better apply the model,it was decided to use Python+Django to build the system.Mainly because Django has a short development cycle,easy to use and easy to integrate with artificial intelligence related programs;secondly,based on storage needs,design the related database table structure;finally,the system is modularized programming to realize the various functions of the system.In response to the increase in data volume and the subsequent scalability issues of the system,cloud computing Docker container technology is adopted.This allows rapid deployment of the system and dynamic expansion,and can effectively solve the problem of large-scale data storage.In terms of intelligent authentication and recognition,it combines face recognition technology.Tensorflow Serving is used to deploy the face model for recognition and authentication,which replaces the traditional recognition method and improves the intelligence and security of the system.In improving the accuracy and response speed of face recognition,the Milvus search engine is used.Use Milvus to build a collection,create an index,and quickly return TopK approximate feature vectors and related data,which improves the performance of the system.In order to expand the system functions,combined with EMQ X for Io T message transmission.In response to the instability of the message transmission of the Internet of Things,the lightweight Pub/Sub message middleware EMQ X is introduced,which can collect and store massive industrial data in a centralized manner and has high stability transmission,and data access and routing can also reach the millisecond level.This guarantees the reliability and realtime transmission of Io T messages.This thesis effectively improves the security of system recognition through biometrics,uses Milvus vector search engine to improve system recognition accuracy and shortens recognition time,and combines cloud computing and microservices Docker container technology to facilitate system deployment,while also extending the system in physical With the application of networking,an efficient and intelligent certification information management system was finally developed,and the function and performance of the system were verified through functional tests and performance tests.