CN116594974A - Intelligent meeting APP liveness analysis method based on member behavior log - Google Patents
Intelligent meeting APP liveness analysis method based on member behavior log Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent meeting service, in particular to an intelligent meeting APP liveness analysis method based on member behavior logs, which comprises the following steps: collecting a behavior log of member access APP; based on the existing member basic information of the platform, counting the number of actively authenticated members according to the member dimension factors; counting and authenticating the number of the members according to the member dimension factors; the member liveness is determined by associating the member basic data with the liveness data through the dimension elements; the beneficial effects are as follows: the intelligent meeting APP liveness analysis method based on the member behavior log provided by the invention is a method for analyzing the member liveness by collecting the behavior log of the member accessing APP and combining the basic meeting organization, the area, the sex and the age of the platform to which the member information dimension element belongs.
Description
Technical Field
The invention relates to the technical field of intelligent industry and conference services, in particular to an intelligent industry and conference APP liveness analysis method based on member behavior logs.
Background
The intelligent meeting app is a software app specially used for meeting workers, and a mobile phone can perform work at any time. The system provides a standardized solution according to the difficulty problem of work of a work party, and a mobile office mode of work of the work party is started through application of the intelligent work party app.
In the prior art, the analysis method of the APP liveness of the intelligent meeting is mainly used for evaluating subjective data such as the number of authenticated members, the number of participation occasions of general activities, the number of news report reading and the like in a certain period, and the attraction of the APP of the intelligent meeting to the members and the measurement of the actual effect generated in the members are lack of necessary objective data support, so that the actual effect generated by general service work cannot be truly reflected.
Disclosure of Invention
The invention aims to provide an intelligent meeting APP activity analysis method based on member behavior logs, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent industry meeting APP liveness analysis method based on member behavior logs, the analysis method comprises the following steps:
collecting a behavior log of member access APP;
based on the existing member basic information of the platform, counting the number of actively authenticated members according to the member dimension factors;
counting and authenticating the number of the members according to the member dimension factors;
and the member liveness is determined by associating the member basic data with the liveness data through the dimension elements.
Preferably, when collecting the action log of the member access APP, configuring an APP access log format, and collecting the member access time and member ID information; and counting the member ID, the access date, the access year and the access month according to the date of the member action log, wherein the member is only extracted once a day by a single member, and the extraction is not repeated.
Preferably, when the number of active authenticated members is counted according to the dimension elements of the members by relying on the existing member basic information of the platform, dimension element information is formed based on the member basic information, and an association relationship is established through the member ID and the number of active members; and counting the number of active members according to the dimension factors.
Preferably, when the member number is counted and authenticated by the member dimension element, dimension element information is formed based on the member basic information, and the member number is counted and authenticated.
Preferably, the dimension elements are used for associating the member basic data with the activity data, and when the member activity is determined, the association relationship between the active member and the authenticated member is established through the member dimension elements; liveness of the different dimensional elements is calculated according to the following formula,
membership liveness = liveness authenticated membership number/real name authenticated membership number 100.
Preferably, the member liveness determination includes counting the number of members having a member behavior log, counting the number of authenticated members per base industry, and determining the member liveness by associating the member base data with the liveness data through dimension elements.
Preferably, when the number of the members with the member behavior log is counted, determining an output format of the member access log, and acquiring member id, access time, access URL, client and IP address information; collecting logs, gathering member IDs and access time according to dates, and extracting a single member once without repeating extraction; forming member dimension element information based on the member information table, the dimension element including: the basic industry organization, the region, the sex and the age of the basic industry organization; and according to the association relation between the dimension elements of the resume member and the login member, the number of active members of each dimension element is summarized.
Preferably, when counting the number of authenticated members of each base station worker, acquiring the expiration time, and summarizing the number of authenticated members of each member dimension element according to a member information table, wherein the information comprises: year, month, day, basic industry organization, region, sex, age, and number of authenticated members; making a timing plan, starting at 1 early morning every day, and calculating yesterday certification quantity; the results of the calculations are archived on a daily basis.
Preferably, when determining the member liveness, correlating the calculated result according to the member dimension element information to obtain the number of the active members and the number of the authenticated members of a certain dimension element; according to the formula: the member liveness = liveness authentication member number/real name authentication member number is 100, liveness of different dimension elements is calculated respectively, and sorting is completed and is used as an evaluation reference for evaluating the intelligent industry meeting service field.
Compared with the prior art, the invention has the beneficial effects that:
the intelligent meeting APP liveness analysis method based on the member behavior log provided by the invention is a method for analyzing the member liveness by collecting the behavior log of the member accessing APP and combining the basic meeting organization, the area, the sex and the age of the platform to which the member information dimension element belongs.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions, and advantages of the present invention more apparent, the embodiments of the present invention will be further described in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are some, but not all, embodiments of the present invention, are intended to be illustrative only and not limiting of the embodiments of the present invention, and that all other embodiments obtained by persons of ordinary skill in the art without making any inventive effort are within the scope of the present invention.
Example 1
Referring to fig. 1, the present invention provides a technical solution: an intelligent industry meeting APP liveness analysis method based on member behavior logs, the analysis method comprises the following steps:
1, collecting a behavior log of member access APP.
And 2, based on the existing member basic information of the platform, counting the number of the active authenticated members according to the member dimension factors.
And 3, counting and authenticating the number of the members according to the member dimension factors.
And 4, determining the member liveness by associating the member basic data with the liveness data through the dimension elements.
1. Collecting behavior log of member access APP
1. And configuring an APP access log format, and collecting information such as member access time, member ID and the like.
2. And counting the member ID, the access date, the access year and the access month according to the date of the member action log, wherein the member is only extracted once a day by a single member, and the extraction is not repeated.
2. Based on the existing member basic information of the platform, the number of actively authenticated members is counted according to the member dimension factors
1. And forming dimension element information based on the member basic information, and establishing an association relationship through the member ID and the number of active members.
2. And counting the number of active members according to the dimension factors.
3. Counting and authenticating the number of the members according to the dimension elements of the members
1. And forming dimension element information based on the member basic information, and counting and authenticating the number of the members.
4. Determining member liveness by associating member base data with liveness data by dimension elements
1. And establishing the association relationship between the active member and the authenticated member through the member dimension element.
2. Liveness of the different dimension elements is calculated according to the following formula.
Membership liveness = liveness authenticated membership number/real name authenticated membership number 100.
Determining the member liveness comprises counting the number of members with member behavior logs, counting the number of authenticated members with real names of each base station work, and determining the member liveness;
1. counting member numbers with member behavior log
1. The output format of the member access log is determined, and information such as member id, access time, access URL, client, IP address and the like can be obtained
2. And (3) collecting logs, gathering member IDs according to dates (year/month/day), and extracting only once by a single member without repeating the extraction.
3. Forming member dimension element information based on the member information table, the dimension element including: the basic industry organization, the region, the sex and the age.
4. And according to the association relation between the dimension elements of the resume member and the login member, the number of active members of each dimension element is summarized.
2. Counting the number of authenticated members of each basic industry
1. Acquiring the number of authenticated members of each member dimension element according to the member information table until the expiration time is acquired, wherein the information comprises: year, month, day, basic industry organization, region, sex, age, and number of authenticated members.
2. And (5) making a timing plan, starting at 1 early morning every day, and calculating the yesterday certification quantity.
3. The results of the calculations are archived on a daily basis.
3. Determining member liveness
1. And correlating the first and second part calculation results according to the member dimension element information to obtain the active member number and the authenticated member number of a certain dimension element.
2. According to the formula: the member liveness = liveness authentication member number/real name authentication member number 100 is calculated to obtain liveness of different dimension elements respectively, and sorting is completed and is used as an evaluation reference for evaluating the intelligent industry meeting service field.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. An intelligent industry meeting APP liveness analysis method based on member behavior logs is characterized by comprising the following steps of: the analysis method comprises the following steps:
collecting a behavior log of member access APP;
based on the existing member basic information of the platform, counting the number of actively authenticated members according to the member dimension factors;
counting and authenticating the number of the members according to the member dimension factors;
and the member liveness is determined by associating the member basic data with the liveness data through the dimension elements.
2. The intelligent meeting APP activity analysis method based on member activity log according to claim 1, wherein: when acquiring the behavior log of the member access APP, configuring an APP access log format, and acquiring member access time and member ID information; and counting the member ID, the access date, the access year and the access month according to the date of the member action log, wherein the member is only extracted once a day by a single member, and the extraction is not repeated.
3. The intelligent meeting APP activity analysis method based on member activity log according to claim 1, wherein: when the number of active authenticated members is counted according to the member dimension elements by relying on the existing member basic information of the platform, dimension element information is formed based on the member basic information, and an association relationship is established through the member ID and the number of active members; and counting the number of active members according to the dimension factors.
4. The intelligent meeting APP activity analysis method based on member activity log according to claim 1, wherein: when the number of authenticated members is counted according to the member dimension elements, dimension element information is formed based on the member basic information, and the number of authenticated members is counted.
5. The intelligent meeting APP activity analysis method based on member activity log according to claim 1, wherein: the method comprises the steps that member basic data and active data are associated through dimension factors, and when member liveness is determined, an association relationship between an active member and an authenticated member is established through the dimension factors of the members; liveness of the different dimensional elements is calculated according to the following formula,
membership liveness = liveness authenticated membership number/real name authenticated membership number 100.
6. The intelligent meeting APP activity analysis method based on member activity log according to claim 1, wherein: the determining of the member liveness includes counting the number of members having a member behavior log, counting the number of authenticated members per base industry and meeting, and determining the member liveness by associating the member base data with the liveness data by the dimension elements.
7. The intelligent meeting APP activity analysis method based on member activity log of claim 6, wherein: when the number of the members with the member behavior log is counted, determining the output format of the member access log, and acquiring member id, access time, access URL, client and IP address information; collecting logs, gathering member IDs and access time according to dates, and extracting a single member once without repeating extraction; forming member dimension element information based on the member information table, the dimension element including: the basic industry organization, the region, the sex and the age of the basic industry organization; and according to the association relation between the dimension elements of the resume member and the login member, the number of active members of each dimension element is summarized.
8. The intelligent meeting APP activity analysis method based on member activity log of claim 6, wherein: counting the number of authenticated members of each base station worker meeting, obtaining the number of authenticated members of each member dimension element according to a member information table until the expiration time is obtained, wherein the information comprises: year, month, day, basic industry organization, region, sex, age, and number of authenticated members; making a timing plan, starting at 1 early morning every day, and calculating yesterday certification quantity; the results of the calculations are archived on a daily basis.
9. The intelligent meeting APP activity analysis method based on member activity log according to claim 8, wherein: when determining the member liveness, correlating the calculated result according to the member dimension element information to obtain the number of the active members and the number of the authenticated members of a certain dimension element; according to the formula: the member liveness = liveness authentication member number/real name authentication member number is 100, liveness of different dimension elements is calculated respectively, and sorting is completed and is used as an evaluation reference for evaluating the intelligent industry meeting service field.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117893256A (en) * | 2024-03-14 | 2024-04-16 | 浙江卡赢信息科技有限公司 | Big data-based app user intelligent management system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117893256A (en) * | 2024-03-14 | 2024-04-16 | 浙江卡赢信息科技有限公司 | Big data-based app user intelligent management system |
| CN117893256B (en) * | 2024-03-14 | 2024-05-31 | 浙江卡赢信息科技有限公司 | Big data-based app user intelligent management system |
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