The Back-End Design And Implementation Of Near Infrared Spectroscopy Detection System

In recent years,with the development of science and technology,near infrared spectroscopy detection technology has become more and more mature.At present,there are many near infrared spectroscopy detection devices and supporting computer programs or mobile phone applications on the market,but these devices are expensive or inconvenient to carry.It is difficult to be popularized,and it is difficult for ordinary users to use them daily.However,with the improvement of people’s living standard-s,more and more people begin to care about their skin condition,so people need a portable and affordable near infrared spectroscopy detection equipment.For this reason,Huawei Consumer Cloud Platform decided to introduce a near infrared spectroscopy detection device in the form of mobile phone accessories at a moderate price.This thesis describes the design and implementation of the back-end of the application system corresponding to the near infrared spectroscopy detection de-vice.Based on the principle of near infrared spectroscopy imaging,the system uses front-end and back-end separation to calibrate and analyze the algorithm of near in-frared reflectance spectra of objects,so as to realize the measurement of substances and give the analysis of measurement results.The back-end of the system includes five modules:external module,detection module,skin assessment module,profile module and broker module.The back-end uses microservice architecture,provides RESTful interface for communication,uses Spring Boot to build the system,uses MySQL database and Cassandra database for data storage,uses Redis as cache,and uses Kafka for message distribution.In this thesis,the overall demand analysis and design of the system are given,the above modules are introduced,and the important function points are explained in detail.At present,the system is in the pre-production stage.The finished products have been worn and used by the internal staff.After the internal staff use,the user feedback is good.