Research And Implementation Of Elevator Advertisement Intelligent Recommendation Platform Based On Microservices Architecture

With the continuous development of artificial intelligence and the progress of software architecture technology,the traditional data analysis methods,recommendation algorithms and monolithic architecture technology cannot meet the growing needs of users and the expansion of their own functions.In order to realize the intelligent analysis and processing of massive image data collection and data transmission interaction in the elevator,and finally realize the business requirements of personalized recommendation of advertising in the elevator,this paper is based on docker container technology,with the development of high-level Golang language,and learn from go micro.Using the concept of micro service framework,combined with convolution neural network and other algorithms in deep learning,an intelligent recommendation platform is realized.Redesign the whole application architecture and use microservices with independent business functions to replace the traditional monolithic architecture.The communication between microservices is a service discovery mechanism,which ensures data consistency and realizes intelligence through synchronous and asynchronous mechanisms.Building functional services such as image analysis and personalized recommendation,including platform data storage and transmission,abnormal alarm,log and monitoring,automated deployment and other operations and management services to assist the platform's functional services.According to the software engineering research and development process and methods,combined with the business needs of intelligent recommendation platform,the whole research is divided into business modularization,platform database design,data storage service reconstruction,intelligent analysis and identification,and personalized advertising recommendation algorithm.Research and application,platform monitoring and early warning,platform automation deployment.Based on the detailed requirements analysis,each part designs a clear module architecture diagram and flow chart,and provides code display and algorithm analysis for the core steps.Finally,the stability and effectiveness of the platform are verified by various functional tests,stress tests and performance tests.At present,the intelligent recommendation platform designed in this paper has been put into practical application,which solves some shortcomings of the traditional monolithic structure,and is conducive to the current trend that enterprises tend to deploy services on the cloud.Provide a set of standardized development and deployment process for the whole research and development team,saving a lot of resources and research and development and deployment cycle.The test results also show that the platform can better meet the needs of enterprises,and prove the effectiveness of the intelligent recommendation algorithm based on deep learning image analysis.In the case of high concurrency,the recommended platform can also provide better intelligent operation and maintenance guarantee.