Design And Implementation Of Big Data Module Of Smart Pension Cloud Platform

The big data module is an important part of the smart elderly care platform.This module collects,preprocesses,stores,analyzes and applies the data generated during the operation and maintenance of the smart elderly care platform,and has the function of empowering the smart elderly care platform.In order to solve the difficult problem of high real-time data calculation requirements and high requirements for ease of use and convenience of the smart pension platform,this paper proposes an optimal design and implementation plan for the big data module of the smart pension cloud platform.The solution includes a Flink-based real-time data warehouse function and a face recognition function based on an improved MTCNN(multi-task cascaded convolutional network)algorithm,FaceNet framework and Faiss framework.The main work content and innovative achievements of this paper are as follows:Design and implement the real-time data warehouse function of the smart pension cloud platform.At present,the smart elderly care cloud platform adopts a data storage and processing method based on a singleserver MySQL database,which is no longer suitable for scenarios with huge data volume and complex composition.This paper proposes a Flinkbased real-time data warehouse function based on the characteristics of high real-time data requirements of the smart elderly care cloud platform and the data format is mostly streaming data.The warehouse is divided into four layers:ODS(original data layer),DWD(detailed data layer),DWS(service data layer),DIM(dimension layer).The ODS layer collects data from various sources and integrates it into a unified format.The DWD layer transforms and cleans data to ensure consistency and quality.The DWS layer aggregates and summarizes data for reporting and analysis purposes.Finally,the DIM layer stores dimensional data such as users and service information for efficient query and analysis.The test results show that the real-time data warehouse of the smart pension cloud platform can run normally and stably and can significantly improve the data storage and analysis capabilities of the smart pension cloud platform.Design and implement the face recognition function of the smart pension cloud platform.At present,the smart elderly care cloud platform adopts a login method based on inputting user names and passwords,which is somewhat unfriendly to elderly users.In order to improve the security,convenience and service level of the smart elderly care cloud platform,and provide better elderly care services for the elderly,this paper proposes a face recognition function based on the improved MTCNN algorithm,FaceNet framework and Faiss framework.The function consists of four stages:face detection,alignment,feature extraction,and matching.The face detection stage uses the improved MTCNN algorithm to detect the presence of faces in images or video streams.The alignment stage corrects the orientation and position of faces for better feature extraction.The feature extraction stage uses FaceNet to extract unique features of faces.Finally,the matching stage uses Faiss to compare the extracted features with a database of known faces to identify individuals.In order to improve the efficiency of the MTCNN face detection algorithm in the smart elderly care cloud platform and reduce the false detection rate,this paper proposes a method to guide the detection frame regression process by introducing prior knowledge.By using prior knowledge to constrain the size and proportion of the generated candidate boxes,unnecessary calculations are reduced,detection efficiency is improved,and false detection rates are reduced.The performance test results of the face recognition function show that the face detection time using the improved MTCNN algorithm has decreased by 29.1%to 42.0%,and the accuracy of face recognition has increased by 6.8%to 8.6%.