A data analysis and processing method

Abstract

The present invention discloses a data analysis and processing method, which belongs to the field of data processing technology and includes the following steps: S1, collecting historical access information of a website; S2, determining a dynamic user set of the website based on the historical access information of the website; S3, obtaining access data of each dynamic user in the dynamic user set, and generating content of interest for the website. The data analysis and processing method collects historical access information of users who have visited the website within a period of time, extracts relatively active historical access user names as a dynamic user set based on the historical access records of each historical access user name; uses the access records of each historical access user name in the dynamic user set as a basis to extract the content of interest of the website in a more targeted manner; the content of interest generated at last can reflect the articles that users are more inclined to read when using the website, which is convenient for website managers to understand user portraits, manage websites, and provide users with a better browsing experience.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a data analysis and processing method. Background Art

[0002] Traditional web pages display information in a large and comprehensive way, with a huge amount of information and not easy to view, which often prevents users from accurately obtaining the information they need, greatly reducing user efficiency. In the Internet era where user-centered design concepts are deeply rooted in people's hearts, traditional websites are also in urgent need of keeping up with the pace of personalized intelligent services. According to the user's access data within the website, the content that users are really interested in on the website can be determined, so that website managers can make timely adjustments to the website content layout. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a data analysis and processing method.

[0004] The technical solution of the present invention is: a data analysis and processing method comprises the following steps:

[0005] S1. Collect historical visit information of the website;

[0006] S2. Determine the dynamic user set of the website based on the historical access information of the website;

[0007] S3. Obtain access data of each dynamic user in the dynamic user set and generate interesting content for the website.

[0008] Furthermore, in S1, the historical access information of the website includes articles read by each historical access user name on the website and access modes of each historical access user name; wherein the access mode includes login access and visitor access.

[0009] Furthermore, S2 includes the following sub-steps:

[0010] S21, constructing a digital access queue for each historical access user name according to the articles read by each historical access user name on the website;

[0011] S22, based on the digital access queue of the historical access user name, obtain the digital access quantity of the historical access user name;

[0012] S23, determining the load coefficient between the historical access user name and the website according to the digital access volume and access mode of each historical access user name;

[0013] S24. The historical access user names with the top 50% load coefficients are used as the dynamic user set.

[0014] The beneficial effect of the above further scheme is: in the present invention, after multiple users visit the website and click to read articles, they will leave a visit record on the website; the present invention collects the historical visit information left by the user visits, and determines the more active users (that is, the loading coefficient is sorted from large to small, ranking the top 50%) according to the reading time of each article and the number of visits to the website by each historical visit user name in the historical visit information, as a dynamic user set. The dynamic user set uses the website for a longer time and with a higher frequency, which can provide a basis for the website to collect content of interest to users and ensure that the extracted content is accurate and targeted.

[0015] Furthermore, in S21, the expression of the digital access queue Task is:

[0016] ; In the formula, M represents the number of times the historical user name visits the website, and N represents the number of articles read by the historical user name each time it visits the website. Indicates the actual reading time of the first article read by the historical user name during the first visit to the website. Indicates the actual reading time of the Nth article read by the historical user name during the first visit to the website. Indicates the actual reading time of the first article read by the historical user name during the second visit to the website. Indicates the actual reading time of the Nth article read by the historical user name during the second visit to the website. Indicates the actual reading time of the first article read by the historical user name during the mth visit to the website. It indicates the actual reading time of the Nth article read by the historical user name during the mth visit to the website. Indicates the actual reading time of the first article read by the historical user name during the Mth visit to the website. Indicates the actual reading time of the Nth article read by the historical visiting username during the Mth visit to the website.

[0017] The digital access queue is mainly used to record the number of times a historical user name has visited the website and the number of articles read each time the user visits the website. It is also necessary to record the reading time of each article read each time the user visits the website.

[0018] Furthermore, in S22, the calculation formula of the digital visit volume V is:

[0019] ; In the formula, M represents the number of times the historical user name visits the website, and N represents the number of articles read by the historical user name each time it visits the website. It indicates the actual reading time of the nth article read by the historical user name during the mth visit to the website. T represents the estimated reading time of the nth article read by the historical user name during the mth visit to the website, m represents the total visit time of the mth visit to the website by the historical visitor name, e represents the index, Gap m Indicates the interval between the end of the mth visit to the website by the historical visiting username and the start of the next visit to the website.

[0020] Further, S23 includes the following sub-steps:

[0021] S231, obtaining the first byte time and first screen loading time of the website;

[0022] S232, determining a time exchange coefficient between the historically visited user name and the website according to the access mode of the historically visited user name and the first byte time and first screen loading time of the website;

[0023] S232: Based on the digital visits of the historically visited user names, the time exchange coefficient between the historically visited user names and the website is adjusted to determine the loading coefficient between the historically visited user names and the website.

[0024] First Byte Time refers to the time when data transmission to the client browser begins. First Screen Load Time: It refers to the time it takes for the browser to display the first screen of the page. The first screen refers to the area of ​​the web page that fits in the browser window without the user having to scroll down. This is the first content that the user sees.

[0025] Furthermore, in S232, the calculation formula of the time exchange coefficient C between the historically visited user name and the website is:

[0026] Where, T byte Indicates the first byte time of the website, T screen Indicates the first screen loading time. represents the rounding up operation, and γ represents the weight coefficient corresponding to the access mode of the historical access username.

[0027] The weight coefficient corresponding to the user's access mode can be set manually, and the requirement is that the weight coefficient of login access is higher than the weight coefficient of visitor access. For example, the weight coefficient of login access is 3, and the weight coefficient of visitor access is 2.

[0028] Furthermore, in S233, the calculation formula of the loading coefficient L between the historically visited user name and the website is:

[0029] ; In the formula, V represents the digital visits of the historical visiting username, and C represents the time exchange coefficient between the historical visiting username and the website.

[0030] Furthermore, in S3, the specific method for generating interesting content for the website is: extracting articles read by each dynamic user in the dynamic user set, generating an interesting article set, and extracting keywords from the title of each article in the interesting article set as the interesting content of the website.

[0031] The beneficial effects of the present invention are: the data analysis and processing method collects the historical access information of users who visit the website within a period of time, and extracts the more active historical access user names as the dynamic user set based on the historical access records of each historical access user name; the access records of each historical access user name in the dynamic user set are used as the basis to more specifically extract the interesting content of the website; the finally generated interesting content can reflect the articles that users prefer to read when using the website, which is convenient for website managers to understand user portraits, manage websites, and provide users with a better browsing experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Flowchart of the data analysis and processing method. DETAILED DESCRIPTION

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

[0034] like Figure 1 As shown, the present invention provides a data analysis and processing method, comprising the following steps:

[0035] S1. Collect historical visit information of the website;

[0036] S2. Determine the dynamic user set of the website based on the historical access information of the website;

[0037] S3. Obtain access data of each dynamic user in the dynamic user set and generate interesting content for the website.

[0038] In the embodiment of the present invention, in S1, the historical access information of the website includes the articles read by each historical access user name on the website and the access mode of each historical access user name; wherein the access mode includes login access and visitor access.

[0039] In this embodiment of the present invention, S2 includes the following sub-steps:

[0040] S21, constructing a digital access queue for each historical access user name according to the articles read by each historical access user name on the website;

[0041] S22, based on the digital access queue of the historical access user name, obtain the digital access quantity of the historical access user name;

[0042] S23, determining the load coefficient between the historical access user name and the website according to the digital access volume and access mode of each historical access user name;

[0043] S24. The historical access user names with the top 50% load coefficients are used as the dynamic user set.

[0044] In the present invention, after multiple users visit a website and click to read an article, a visit record will be left on the website; the present invention collects the historical visit information left by the user visit, and determines the more active users (i.e., the loading coefficient is sorted from large to small, ranking the top 50%) according to the reading time of each article and the number of visits to the website by each historical visit user name in the historical visit information, as a dynamic user set. The dynamic user set uses the website for a longer time and with a higher frequency, which can provide a basis for the website to collect the content of interest to users and ensure that the extracted content is accurate and targeted.

[0045] In the embodiment of the present invention, in S21, the expression of the digital access queue Task is:

[0046] ; In the formula, M represents the number of times the historical user name visits the website, and N represents the number of articles read by the historical user name each time it visits the website. Indicates the actual reading time of the first article read by the historical user name during the first visit to the website. Indicates the actual reading time of the Nth article read by the historical user name during the first visit to the website. Indicates the actual reading time of the first article read by the historical user name during the second visit to the website. Indicates the actual reading time of the Nth article read by the historical user name during the second visit to the website. Indicates the actual reading time of the first article read by the historical user name during the mth visit to the website. It indicates the actual reading time of the Nth article read by the historical user name during the mth visit to the website. Indicates the actual reading time of the first article read by the historical user name during the Mth visit to the website. Indicates the actual reading time of the Nth article read by the historical visiting username during the Mth visit to the website.

[0047] The digital access queue is mainly used to record the number of times a historical user name has visited the website and the number of articles read each time the user visits the website. It is also necessary to record the reading time of each article read each time the user visits the website.

[0048] In the embodiment of the present invention, in S22, the calculation formula of the digital visit volume V is:

[0049] ; In the formula, M represents the number of times the historical user name visits the website, and N represents the number of articles read by the historical user name each time it visits the website. It indicates the actual reading time of the nth article read by the historical user name during the mth visit to the website. T represents the estimated reading time of the nth article read by the historical user name during the mth visit to the website, m represents the total visit time of the mth visit to the website by the historical visitor name, e represents the index, Gap m Indicates the interval between the end of the mth visit to the website by the historical visiting username and the start of the next visit to the website.

[0050] In this embodiment of the present invention, S23 includes the following sub-steps:

[0051] S231, obtaining the first byte time and first screen loading time of the website;

[0052] S232, determining a time exchange coefficient between the historically visited user name and the website according to the access mode of the historically visited user name and the first byte time and first screen loading time of the website;

[0053] S232: Based on the digital visits of the historically visited user names, the time exchange coefficient between the historically visited user names and the website is adjusted to determine the loading coefficient between the historically visited user names and the website.

[0054] First Byte Time refers to the time when data transmission to the client browser begins. First Screen Load Time: It refers to the time it takes for the browser to display the first screen of the page. The first screen refers to the area of ​​the web page that fits in the browser window without the user having to scroll down. This is the first content that the user sees.

[0055] In the embodiment of the present invention, in S232, the calculation formula of the time exchange coefficient C between the historically visited user name and the website is:

[0056] Where, T byte Indicates the first byte time of the website, T screen Indicates the first screen loading time. represents the rounding up operation, and γ represents the weight coefficient corresponding to the access mode of the historical access username.

[0057] The weight coefficient corresponding to the user's access mode can be set manually, and the requirement is that the weight coefficient of login access is higher than the weight coefficient of visitor access. For example, the weight coefficient of login access is 3, and the weight coefficient of visitor access is 2.

[0058] In the embodiment of the present invention, in S233, the calculation formula of the loading coefficient L between the historically visited user name and the website is:

[0059] ; In the formula, V represents the digital visits of the historical visiting username, and C represents the time exchange coefficient between the historical visiting username and the website.

[0060] In the embodiment of the present invention, in S3, the specific method for generating interesting content for the website is: extracting articles read by each dynamic user in the dynamic user set, generating an interesting article set, and extracting keywords from the title of each article in the interesting article set as the interesting content of the website.

[0061] 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 should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A data analysis and processing method, characterized in that: The following steps are involved: S1. Collect historical visit information of the website; S2. Determine the dynamic user set of the website based on the historical access information of the website; S3, obtaining access data of each dynamic user in the dynamic user set, and generating interesting content for the website; The S2 comprises the following sub-steps: S21, constructing a digital access queue for each historical access user name according to the articles read by each historical access user name on the website; S22, based on the digital access queue of the historical access user name, obtain the digital access quantity of the historical access user name; S23, determining the load coefficient between the historical access user name and the website according to the digital access volume and access mode of each historical access user name; S24, taking the historical access user names ranked in the top 50% of the load coefficient as the dynamic user set; In S21, the expression of the digital access queue Task is: ; In the formula, M represents the number of times the historical user name visits the website, and N represents the number of articles read by the historical user name each time it visits the website. Indicates the actual reading time of the first article read by the historical user name during the first visit to the website. Indicates the actual reading time of the Nth article read by the historical user name during the first visit to the website. Indicates the actual reading time of the first article read by the historical user name during the second visit to the website. Indicates the actual reading time of the Nth article read by the historical user name during the second visit to the website. Indicates the actual reading time of the first article read by the historical user name during the mth visit to the website. It indicates the actual reading time of the Nth article read by the historical user name during the mth visit to the website. Indicates the actual reading time of the first article read by the historical user name during the Mth visit to the website. Indicates the actual reading time of the Nth article read by the historical user name during the Mth visit to the website; In S22, the calculation formula of the digital visit volume V is: ; In the formula, M represents the number of times the historical user name visits the website, and N represents the number of articles read by the historical user name each time it visits the website. It indicates the actual reading time of the nth article read by the historical user name during the mth visit to the website. T represents the estimated reading time of the nth article read by the historical user name during the mth visit to the website, m represents the total visit time of the mth visit to the website by the historical visitor name, e represents the index, Gap m Indicates the interval between the end of the mth visit to the website by the historical user name and the start of the next visit to the website; The S23 comprises the following sub-steps: S231, obtaining the first byte time and first screen loading time of the website; S232, determining a time exchange coefficient between the historically visited user name and the website according to the access mode of the historically visited user name and the first byte time and first screen loading time of the website; S232, based on the digital visits of the historical visiting user name, adjusting the time exchange coefficient between the historical visiting user name and the website, and determining the loading coefficient between the historical visiting user name and the website; In S232, the calculation formula of the time exchange coefficient C between the historically visited user name and the website is: Where, T byte Indicates the first byte time of the website, T screen Indicates the first screen loading time. represents the round-up operation, and γ represents the weight coefficient corresponding to the access mode of the historical access user name; In S233, the calculation formula of the loading coefficient L between the historical access user name and the website is: ; In the formula, V represents the digital visits of the historical visiting username, and C represents the time exchange coefficient between the historical visiting username and the website.

2. The data analysis and processing method according to claim 1, characterized in that: In S1, the historical access information of the website includes the articles read by each historical access user name on the website and the access mode of each historical access user name; wherein the access mode includes login access and visitor access.

3. The data analysis and processing method according to claim 1, characterized in that: In S3, the specific method for generating interesting content for the website is: extracting articles read by each dynamic user in the dynamic user set to generate an interesting article set, and extracting keywords from the title of each article in the interesting article set as the interesting content of the website.

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