Design and Implementation of Port Data Analysis and Visualization System

Taizhi Lv; Peiyi Tang

doi:10.54691/fse.v2i11.2991

Design and Implementation of Port Data Analysis and Visualization System

Taizhi Lv, Peiyi Tang

Frontiers in Science and Engineering, (2022), Vol.2, No.11, pp.112-117

Published: November 22, 2022

DOI: 10.54691/fse.v2i11.2991

PDF: Download Full Text PDF

Abstract

Under the background of big data era, ports have realized the informatization of transport data, and has accumulated a large amount of transport data. The potential value contained in the port transportation data needs to be excavated, and a large number of external data also needs to be obtained. However, the traditional statistical analysis system can no longer meet the demand. In order to fully tap the potential value of port data, this paper designs and implements a port data analysis and visualization system to help enterprises master the trend of material flow. The system development is based on Python language. Pandas is used for data analysis, Flask is used to implement the MVC framework, ECharts is used for chart display, and data is imported from the business database to the MySQL database.

Keywords: Big Data Technology, Data Analysis, Data Visualization, Data Cleaning, Flask, Pandas

APA Citation: Taizhi Lv, Peiyi Tang (2022). Design and Implementation of Port Data Analysis and Visualization System. Frontiers in Science and Engineering, 2(11), 112-117. https://doi.org/10.54691/fse.v2i11.2991

References

Panahi, Roozbeh, et al. "Developing a resilience assessment model for critical infrastructures: The case of port in tackling the impacts posed by the Covid-19 pandemic." Ocean & Coastal Management 226 (2022): 106240.
Vogel, Patrick, et al. "A low-effort analytics platform for visualizing evolving Flask-based Python web services." 2017 IEEE Working Conference on Software Visualization (VISSOFT). IEEE, 2017: 109-113.
Lemenkova, Polina. "Processing oceanographic data by Python libraries NumPy, SciPy and Pandas." Aquatic Research 2.2 (2019): 73-91.
Li, Deqing, et al. "ECharts: a declarative framework for rapid construction of web-based visualization." Visual Informatics 2.2 (2018): 136-146.
Wu, Mingxuan, et al. "Epidemic Data Visualization Surveillance Based on Flask." 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA). IEEE, 2022:1147-1151.
Novac, Ovidiu Constantin, et al. "Comparative study of some applications made in the Angular and Vue. js frameworks." 2021 16th International Conference on Engineering of Modern Electric Systems (EMES). IEEE, 2021: 1-4.