A sales data visualization method based on big data

Abstract

The present invention discloses a sales data visualization method based on big data, belonging to the field of data processing technology, comprising the following steps: S1: determining the representative preference of each sales item in a liquor sales report; S2: using the position of the representative preference of each sales item as the area to be rendered; S3: using the scroll bar of a scrolling container to render the area to be rendered of each sales item in the liquor sales report to obtain a primary visualized liquor sales report; S4: processing the primary visualized liquor sales report to obtain a final visualized liquor sales report. The present invention further updates the primary visualized report to make it a perspective report, which better meets the actual needs of users.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular relates to a sales data visualization method based on big data. Background Art

[0002] In the liquor sales sector, companies generate massive amounts of sales data daily. This data contains a wealth of information, such as liquor sales figures, liquor sales channels, customer preferences, and product categories. Liquor preferences vary significantly among users in different regions, making it crucial to effectively analyze user preferences.

[0003] To more effectively utilize sales data and improve the scientific nature and accuracy of sales decisions, big data-based sales data visualization methods have emerged. Sales report visualization processes and analyzes massive amounts of sales data, annotating key information, such as user representative preferences recorded in sales reports. Quickly extracting key information from these preferences can help sales staff and management quickly understand sales trends, identify potential business opportunities, and optimize sales strategies.

[0004] However, most existing sales visualization methods have the following problems: First, the data presentation method is single and lacks interactivity; second, the data rendering effect is poor, especially for sales reports containing a large number of sales items. It is impossible to extract representative content from the user's representative preferences, and the rendering efficiency of representative content is low, which affects the user experience; third, the visualization effect is limited, and it is difficult to fully display the complexity and diversity of sales data. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a sales data visualization method based on big data.

[0006] The technical solution of the present invention is: a sales data visualization method based on big data comprises the following steps:

[0007] S1. Determine the representative preference of each sales item in the liquor sales report;

[0008] S2. taking the position of the representative preference of each sales item as the area to be rendered;

[0009] S3. Rendering the to-be-rendered area of each sales item in the sales report using the scroll bar of the scroll container to obtain a primary visual liquor sales report;

[0010] S4. Process the primary visualized liquor sales report to obtain a final visualized liquor sales report.

[0011] Furthermore, S1 includes the following sub-steps:

[0012] S11. Obtain customer behavior information for each sales item in the liquor sales report;

[0013] S12. Calculate the target weight of each word in the sales item based on the customer behavior information of the sales item;

[0014] S13. After sorting the target weights from large to small, the top K words are used as representative preferences for sales items, where K represents the number of words included in the preset representative preferences.

[0015] The beneficial effect of the above further scheme is: in the present invention, the target weight of each word in the customer behavior information recorded in the sales item (such as the user's purchase frequency and purchase habits and other data) is calculated, and the word with the largest weight is selected as the representative preference of the sales item, which can objectively identify the most representative information in the sales item and avoid the subjectivity and uncertainty of human selection. The introduction of the sparsity parameter in the target weight helps to deal with the problem of vocabulary sparsity that may appear in the sales item, that is, some words may not be common in the sales item, but their appearance is crucial to understanding the meaning of the sales item. By adjusting the value of the sparsity parameter, the influence of common and rare words in the weight calculation can be balanced. By using word vectors to calculate the Euclidean distance, the semantic similarity between words can be captured, and the meaning of the sales item and the customer's preference can be understood more accurately.

[0016] Furthermore, in S12, the target weight P of the i-th word in the sales item i The calculation formula is: ; Where α represents the sparsification parameter, D i_NAME represents the Euclidean distance between the word vector of the i-th word in the sales entry and the word vector of the product name of the sales entry, D i_REPORT Represents the Euclidean distance between the word vector of the i-th word in the sales entry and the word vector of the name of the sales report.

[0017] Furthermore, S3 includes the following sub-steps:

[0018] S31. Build a scroll container;

[0019] S32: Determine the monitoring duration for the scrolling container to complete the virtual scrolling;

[0020] S33. During the monitoring time, the scroll bar of the scroll container is used to virtually scroll the to-be-rendered area of each sales item in the sales report, and the rendering is completed to obtain a primary visual liquor sales report.

[0021] The beneficial effect of the above further scheme is: in the present invention, virtual scrolling is a technology for optimizing the rendering performance of long lists, which reduces the number of DOM nodes and improves the scrolling response speed by rendering only the elements in the visible area. As a visual interface element, the scrolling container allows users to browse a large amount of sales data through the scroll bar, realizes the dynamic loading of data, and reduces the resources and time required for loading and rendering a large amount of data at one time. In addition, by reasonably setting the monitoring time, it can be ensured that when the user scrolls to a new data area, there is enough time to load and render the data in the area, thereby avoiding delays or freezes in data loading. During the monitoring time, the scroll bar of the scrolling container is used to render the area to be rendered for each sales item in the sales report.

[0022] Furthermore, in S31, the starting index K of the scroll container start The calculation formula is: Where p y Indicates the pixel value of the vertical scrolling of the scroll bar of the scroll container. H represents the height of the sales item in the sales report. Represents the floor function.

[0023] Furthermore, in S32, the calculation formula of the monitoring time T of the virtual scrolling is: Where V max Indicates the maximum reasonable scrolling speed of the scroll container, V indicates the current scrolling speed of the scroll container, t * Indicates the basic listening interval of the scroll container.

[0024] Calculate the time from when the scroll event is triggered to when the rendering is completed as the monitoring duration. The basic monitoring interval of the scroll container usually refers to the time interval set in the scroll event monitoring.

[0025] Furthermore, in S33, the number of buffer items N during virtual scrolling is buffer The calculation formula is: ; In the formula, min(·) represents the minimum function, L represents the width of the sales report, and M represents the length of the sales report. Represents the ceiling function.

[0026] Buffer items refer to additional rendering items outside the visible area to prevent blank spaces from appearing when scrolling quickly.

[0027] Furthermore, in S4, the Tableau tool is used to update the primary visual liquor sales report to obtain the final visual liquor sales report.

[0028] The beneficial effects of the present invention are:

[0029] (1) The present invention discloses a sales data visualization method based on big data, which extracts key information and trends from sales data, such as hot-selling products, peak sales periods, and customer preferences recorded in customer behavior information. By determining representative preferences, it helps to quickly identify sales highlights;

[0030] (2) The present invention maps representative preferences to specific areas to be rendered, which is the key point of data visualization. The scroll bar of the scroll container is used to render the areas to be rendered for each sales item in the sales report. The dynamic rendering method can not only improve the response speed of visualization, but also accurately mark important information.

[0031] (3) The present invention further updates the primary visual report to make it a perspective report that better meets the actual needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The flowchart of the sales data visualization method based on big data. DETAILED DESCRIPTION

[0033] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0034] like Figure 1 As shown, the present invention provides a sales data visualization method based on big data, comprising the following steps:

[0035] S1. Determine the representative preference of each sales item in the liquor sales report;

[0036] S2. taking the position of the representative preference of each sales item as the area to be rendered;

[0037] S3. Rendering the to-be-rendered area of each sales item in the liquor sales report using the scroll bar of the scroll container to obtain a primary visualized liquor sales report;

[0038] S4. Process the primary visualized liquor sales report to obtain a final visualized liquor sales report.

[0039] In this embodiment of the present invention, S1 includes the following sub-steps:

[0040] S11. Obtain customer behavior information for each sales item in the liquor sales report;

[0041] S12. Calculate the target weight of each word in the sales item based on the customer behavior information of the sales item;

[0042] S13. After sorting the target weights from large to small, the top K words are used as representative preferences for sales items, where K represents the number of words included in the preset representative preferences.

[0043] In the present invention, the target weight of each word in the customer behavior information recorded in the sales item (such as the user's purchase frequency and purchasing habits, etc.) is calculated, and the word with the largest weight is selected as the representative preference of the sales item. This can objectively identify the most representative information in the sales item and avoid the subjectivity and uncertainty of human selection. The introduction of the sparsity parameter in the target weight helps to deal with the problem of vocabulary sparsity that may appear in the sales item, that is, some words may not be common in the sales item, but their appearance is crucial to understanding the meaning of the sales item. By adjusting the value of the sparsity parameter, the influence of common and rare words in the weight calculation can be balanced. By using word vectors to calculate the Euclidean distance, the semantic similarity between words can be captured, and the meaning of the sales item and the customer's preference can be understood more accurately.

[0044] In the embodiment of the present invention, in S12, the target weight P of the i-th word in the sales item is i The calculation formula is: ; Where α represents the sparsification parameter, D i_NAME represents the Euclidean distance between the word vector of the i-th word in the sales entry and the word vector of the product name of the sales entry, D i_REPORT Represents the Euclidean distance between the word vector of the i-th word in the sales entry and the word vector of the name of the sales report.

[0045] In this embodiment of the present invention, S3 includes the following sub-steps:

[0046] S31. Build a scroll container;

[0047] S32: Determine the monitoring duration for the scrolling container to complete the virtual scrolling;

[0048] S33. During the monitoring time, the scroll bar of the scroll container is used to virtually scroll the to-be-rendered area of each sales item in the sales report, and the rendering is completed to obtain a primary visual liquor sales report.

[0049] In the present invention, virtual scrolling is a technology that optimizes the rendering performance of long lists. It reduces the number of DOM nodes and improves the scrolling response speed by rendering only the elements in the visible area. As a visual interface element, the scrolling container allows users to browse a large amount of sales data through the scroll bar, realizes the dynamic loading of data, and reduces the resources and time required for one-time loading and rendering of a large amount of data. In addition, by reasonably setting the monitoring time, it can be ensured that when the user scrolls to a new data area, there is enough time to load and render the data in the area, thereby avoiding delays or freezes in data loading. During the monitoring time, the scroll bar of the scrolling container is used to render the area to be rendered for each sales item in the sales report.

[0050] In the embodiment of the present invention, in S31, the starting index K of the scroll container start The calculation formula is: Where p y Indicates the pixel value of the vertical scrolling of the scroll bar of the scroll container. H represents the height of the sales item in the sales report. Represents the floor function.

[0051] In the embodiment of the present invention, in S32, the calculation formula of the monitoring time T of the virtual scrolling is: Where V max Indicates the maximum reasonable scrolling speed of the scroll container, V indicates the current scrolling speed of the scroll container, t * Indicates the basic listening interval of the scroll container.

[0052] Calculate the time from when the scroll event is triggered to when the rendering is completed as the monitoring duration. The basic monitoring interval of the scroll container usually refers to the time interval set in the scroll event monitoring.

[0053] In the embodiment of the present invention, in S33, the number of buffer items N during virtual scrolling is buffer The calculation formula is: ; In the formula, min(·) represents the minimum function, L represents the width of the sales report, and M represents the length of the sales report. Represents the ceiling function.

[0054] Buffer items refer to additional rendering items outside the visible area to prevent blank spaces from appearing when scrolling quickly.

[0055] In an embodiment of the present invention, in S4, the primary visualized liquor sales report is updated using the Tableau tool to obtain a final visualized liquor sales report.

[0056] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A sales data visualization method based on big data, characterized in that: The following steps are involved: S1. Determine the representative preference of each sales item in the liquor sales report; S2. taking the position of the representative preference of each sales item as the area to be rendered; S3. Rendering the to-be-rendered area of each sales item in the liquor sales report using the scroll bar of the scroll container to obtain a primary visualized liquor sales report; S4. Process the primary visualized liquor sales report to obtain a final visualized liquor sales report; The S1 includes the following sub-steps: S11. Obtain customer behavior information for each sales item in the liquor sales report; S12. Calculate the target weight of each word in the sales item based on the customer behavior information of the sales item; S13. After sorting the target weights from largest to smallest, the top K ranked words are used as representative preferences for the sales items, where K represents the number of words included in the preset representative preferences; The S3 includes the following sub-steps: S31. Build a scroll container; S32: Determine the monitoring duration for the scrolling container to complete the virtual scrolling; S33. During the monitoring duration, use the scroll bar of the scroll container to virtually scroll the to-be-rendered area of each sales item in the sales report to complete the rendering, thereby obtaining a primary visual liquor sales report; In the above S32, the calculation formula of the monitoring time T of the virtual scrolling is: Where V max Indicates the maximum reasonable scrolling speed of the scroll container, V indicates the current scrolling speed of the scroll container, t * Indicates the basic listening interval of the scroll container.

2. The sales data visualization method based on big data according to claim 1, characterized in that: In S12, the target weight P of the i-th word in the sales item i The calculation formula is: ; Where α represents the sparsification parameter, D i_NAME represents the Euclidean distance between the word vector of the i-th word in the sales entry and the word vector of the product name of the sales entry, D i_REPORT Represents the Euclidean distance between the word vector of the i-th word in the sales entry and the word vector of the name of the sales report.

3. The sales data visualization method based on big data according to claim 1, characterized in that: In the above S31, the starting index K of the scroll container start The calculation formula is: Where p y Indicates the pixel value of the vertical scrolling of the scroll bar of the scroll container. H represents the height of the sales item in the sales report. Represents the floor function.

4. The sales data visualization method based on big data according to claim 1, characterized in that: In the above S33, the number of buffer items N during virtual scrolling is buffer The calculation formula is: ; In the formula, min(·) represents the minimum function, L represents the width of the sales report, and M represents the length of the sales report. Represents the ceiling function.

5. The sales data visualization method based on big data according to claim 1, characterized in that: In S4, the Tableau tool is used to update the primary visual liquor sales report to obtain the final visual liquor sales report.

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