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Visualization of Consumption Trends and Category Demand Analysis of Prepared Dishes

Author(s)

Shunhua Liu, Xixi Huo, Qingfeng Zhou

Affiliation(s)

College of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, Henan, China

Abstract

To address the industry pain points in the prepared dish market, such as category homogenization, inaccurate consumption trend prediction, and insufficient regional preference mining, this paper designs and implements a set of visualization system for consumption trends and category demand analysis of prepared dishes by combining big data analysis and front-end and back-end development technologies. The system adopts a full-process architecture of "data processing - data analysis - visualization display". The back-end builds a four-layer data warehouse(ODS,DWD,DWS,ADS)based on Hadoop and Hive, realizes ETL task scheduling through Azkaban, and completes cross-database data synchronization with DataX. The front-end uses Vue.js+ECharts to build a responsive visualization interface, supporting interactive analysis in six dimensions including consumption trends, category demand, and crowd portraits. Test results based on more than 1,200 valid prepared dish consumption data show that the system's query response time is ≤3 seconds and the visualization chart loading time is ≤5 seconds. It can intuitively present market rules and user demand characteristics, provide data-driven decision support for enterprises, and has good practicability and scalability.

Keywords

Prepared Dishes; Data Warehouse; Separation of Front-End and Back-End; Visualization; Multi-dimensional Analysis; Task Scheduling

References

[1]Weiwei Zhang, Qiang Wang. Analysis on the Development Status and Trends of China's Prepared Dishes Industry. Science and Technology of Food Industry, 2021, 42(22):1-7. [2]Dongmei Li, Beiwei Zhu. Inheritance and Innovation of Chinese Prepared Dish Industry. Journal of Chinese Institute of Food Science and Technology, 2022, 22(10): 1-8. [3]Jianguo Wang, Min Liu. Homogenization Competition in the Prepared Dish Market and Its Breakthrough Paths. Journal of Commercial Economics, 2021(18): 135-137. [4]Yunlong Ma, Xiaofeng Zhang. Design and Implementation of a Massive Data Governance Framework Based on Hadoop. Application Research of Computers, 2019, 36(8): 2386-2389. [5]Xiaodong Lin, Gang Zhao. Research on the Application of Hadoop Distributed Storage in Big Data Analysis. Computer Science, 2020, 47(S1): 456-459. [6]Na Li, Qiang Zhang. Design of a Prepared Dish Consumption Demand Analysis System Based on Big Data Visualization. Computer Engineering and Applications, 2023, 59(18): 234-241. [7]Peng Wang, Jie Liu. Design and Practice of a Four-Layer Enterprise-Level Data Warehouse Architecture. Data Analysis and Knowledge Discovery, 2021, 5(3): 102-109. [8]Jing Zhao, Jun Li. Scenario-Based Segmentation Trends and Marketing Strategies in the Consumer Goods Industry. Commercial Research, 2020(10): 89-96. [9]Ming Sun, Li Zhou. Research on the Matching Degree Between Price Hierarchy Distribution and Consumption Demand of Fast-Moving Consumer Goods. Price Theory and Practice, 2021(6): 145-148. [10]Junwen An, Ziying Fang. Development Status, Influencing Factors and Trends of China's Prepared Dish Industry. Journal of Food Science and Technology, 2023, 41(3): 132-140. [11]Lei Gao, Min Wu. Development Opportunities of the Prepared Dish Industry Under the Background of Consumption Upgrade in the Sinking Market. Journal of Commercial Economics, 2022(12): 141-144. [12]Min Zhang, Wei Liu. Research on Prepared Dish Consumer Portraits and Category Preferences—Based on 2023 E-Commerce Data. Journal of Commercial Economics, 2023(15): 156-160.