ABSTRACT ： At present, many enterprises in China have a lot of sales data, and it is
difficult to sort out enterprises. Especially, many enterprises can only use the traditional
EXCEL form for statistical analysis in sales statistics. However, because some tables require a
large amount of data and need to view each page in batches, it is very cumbersome, which
leads to many enterprises can not give people a good visual effect when calculating sales data,
Therefore, big data technology can be used to visualize the effect of large screen and divide the
sales of different types of products. Since many large enterprise branches spread across the
country, big data technology can also be used to visualize the sales quota of different regions.
After processing of large screen visualization, people can count all the data and display the
effect very clearly, It is not necessary to view each page of data separately, which provides
better convenience for the statistical data of today's retail enterprises. Especially when the data
of retail enterprises are relatively disordered and very troublesome to count, the use of big data
technology plays a crucial role in the management and statistics of enterprises. Therefore, this
topic has carried out in-depth research and analysis of supermarket retail data visualization.
This system mainly uses Python language, flask framework, and Yeachat to render visual
data, and integrates jquery framework and plotly to obtain html plotly for design. It mainly
shows the effect of visualization. Users in the front end can view after logging in. The first five
pie charts of sales area data visualization, the first five displays of sales area data visualization,
product type visualization, the first five displays of sales type, the bar chart of sales data
statistics in each region, the visual line chart of sales data, the bar chart of product category
data statistics, and the back end administrator has the functions of user management, modifying
user information, and searching users after logging in.
In terms of design methods, first import the flask framework and the front-end
visualization plotly of the Pyachat rendering, then obtain the html plotly in the plotly through
the API interface, and call the js file. It is important to note that the js script is attached to the
chart html file, and start the configuration of the rendering chart in the chart in the pyecharts,
such as the bar chart, line chart, and other charts, and request the data in the database through
the jQuery framework and pyecharts, In terms of interface, the traditional post interface is used
to retrieve the data in mysql database, and it is defined according to the increment id, and
5星 · 超过95%的资源 63 浏览量
2023-08-22
00:39:58
上传
评论 1
收藏 506KB DOCX 举报 
weixin_494872832024-04-25资源不错，对我启发很大，获得了新的灵感，受益匪浅。
