Article Details

Abstract

Aiming at the pain points in life service mini-programs, including inconsistent cross-terminal experience, high-concurrency response delays, and scattered functions, this study designs and implements a cross-platform “Life All-in-One” mini-program based on uni-app. The system adopts a three-tier architecture of “frontend-backend-data”: the frontend achieves multi-terminal adaptation through uni-app, the backend uses Kafka and Flink to build a high-concurrency stream processing pipeline, and the data layer employs MySQL and Redis for hierarchical storage. It integrates core modules such as travel services, daily life services, and smart tools to meet users' one-stop needs. Tests show that the mini-program achieves 97.8% cross-platform rendering consistency, an average frame rate of 59.2 fps, no request loss under 500 concurrent requests, and a response time of less than 3 seconds, with stable performance. This solution effectively addresses the core problems of cross-terminal adaptation and high concurrency, providing a reusable architectural reference for similar comprehensive life service mini-programs.

Keywords

uni-app; cross-platform; high-concurrency processing; stream processing; life services

References

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Funding

College Students' Innovation and Entrepreneurship Training Program for the 2024-2025 Academic Year (XJ202313714457)

About the authors

Jiang Fangweiyue (2005-), female, Han ethnicity, from Jiaxing, Zhejiang, is currently pursuing a bachelor's degree with a research focus on data science and big data technology; Zhang Haohan (2005-), female, Han ethnicity, from Liaocheng, Shandong Province, undergraduate studies, research direction: big data; He Chang (1996-), female, Manchu, from Mudanjiang, Heilongjiang Province, teacher, master's student, research direction: Convolutional Neural Networks, Causal Inference, Survival Analysis and Risk Prediction; Corresponding author: Ji Lijian (1981-), male, Han nationality, from Harbin, Heilongjiang Province, lecturer, engineer, teacher, doctoral student, research direction: software engineering, big data, artificial intelligence.