Multi-terminal-based one-network-to-office event tracking service method

Abstract

The invention discloses a multi-terminal-based one-network-communication point burying service method, relates to the technical field of data analysis and processing, and solves the problem that data obtained through point burying often comes from different systems or different suppliers and has no uniform point burying rule, so that format errors are easy to occur in the application process of the data. Acquiring first behavior data when a user accesses the platform; s2, making a data cleaning rule, and cleaning the first behavior data of the user to obtain first standard behavior data of the user; s3, analyzing the first standard behavior data of the user, and optimizing the platform according to an analysis result; and S4, obtaining second behavior data in the optimized platform access process of the user, analyzing the second behavior data, and obtaining a satisfaction score of the user through analysis. The optimization of the obtained data is realized through a unified burying point rule.

Description

Technical Field

[0001] The present invention belongs to the technical field of data analysis and processing, and specifically relates to a one-network-one-service clickstream service method based on multiple terminals. Background Art

[0002] One-network-one-service provides users with a convenient one-stop service handling channel through a unified digital platform. It integrates various government or enterprise services, administrative services, etc. on a single platform, allowing users to handle various affairs online through this platform, such as administrative approval, service application, information query, etc. Through "one-network-one-service", users do not need to run around or visit multiple departments frequently, and can complete various matters efficiently and conveniently, improving the handling efficiency and optimizing the service experience. This platform usually supports access from multiple terminals (such as PC terminals, mobile terminals, mini-programs, etc.) to ensure that users can operate at any time and place.

[0003] In the prior art, the data obtained through clickstream often comes from different systems or different suppliers, and there is no unified clickstream rule, resulting in easy format errors in the application of data. At the same time, when the platform needs to be optimized, there is no intuitive data for platform optimization; Therefore, the present invention proposes a one-network-one-service clickstream service method based on multiple terminals. Summary of the Invention

[0004] The purpose of the present invention is to propose a one-network-one-service clickstream service method based on multiple terminals to solve the problems mentioned in the above background art.

[0005] The technical problem to be solved by the present invention is: How to optimize the platform with the data obtained after unifying the clickstream rules.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A one-network-one-service clickstream service method based on multiple terminals, the method is as follows: Step S1, obtain the first behavior data when the user accesses the platform; Step S2, formulate data cleaning rules, clean the first behavior data of the user, and obtain the first standard behavior data of the user after cleaning; Step S3, analyze the first standard behavior data of the user, and optimize the platform according to the analysis results; Step S4, obtain the second behavior data during the process of the optimized user accessing the platform, and analyze the second behavior data to obtain the user's satisfaction score.

[0007] Further, the step S1 includes the following sub-steps: Step S11, create the trigger rules for page view events and click events within the platform; Step S12, mark the page view timestamp when the user first visits any page within the platform as the first page view timestamp. When the user jumps to other pages within the platform or exits the platform page, mark the timestamp as the second page view timestamp. Subtract the first page view timestamp from the second page view timestamp to obtain the access duration of the user visiting any page within the platform; Step S13, when all users trigger a click event on any page, obtain the trigger count of each button, and then obtain the total trigger count of all users triggering click events on this page. Divide the trigger count of the button by the total trigger count to obtain the trigger frequency of this button; Step S14, obtain the number of first active users within the past week in the platform and record the user IDs of all users; Step S15, record the access duration of the user visiting any page within the platform, the trigger frequency of any button within the page, and the number of first active users as the first behavioral data of the user.

[0008] Further, when any of the fields "event_type='page_view'", "page_url", and "referrer_url" exist in the event fields of the buried point event, mark this buried point event as a page view event and record the corresponding page view timestamp when the page view event is triggered; When any of the fields "event_type='click'", "element_id", and "page_url" exist in the event fields of the buried point event, mark this buried point event as a click event, indicating that the user triggers a click event by clicking a button within the page.

[0009] Further, the said Step S2 includes the following sub-steps: Step S21, formulate the access duration rule for users, specifically: If the access duration of the user visiting any page within the platform is less than or equal to the duration threshold, it is determined that the user has made a misoperation or there is a page loading error, and the access duration less than or equal to the duration threshold is excluded; If the access duration of the user visiting any page within the platform is greater than the duration threshold, no operation is performed; Step S22, formulate the trigger frequency rule for any button within the page and capture the abnormal trigger frequency of the button according to the trigger frequency rule, specifically: If the trigger frequency of any button within the page is less than zero or greater than one, it is determined that the trigger frequency of the corresponding button is abnormal, and the abnormal trigger frequency is recorded; If the trigger frequency of any button on the page is greater than or equal to zero and less than or equal to one, no operation is performed.

[0010] Furthermore, step S2 further includes the following sub-steps: Step S23, formulate user activity rules, and optimize the platform according to the user activity rules, specifically: When the number of first active users in the past week on the platform is less than zero, or the number of first active users in the past week on the platform is not an integer, optimize the platform, re-obtain the user ID, and record the number of active user IDs in the past week as the number of first active users; Step S24, record the access duration rule of the user, the trigger frequency rule of any button on the page, and the user activity rule as data cleaning rules; Step S25, clean the first behavior data of the user according to the data cleaning rules to obtain the first standard behavior data of the user.

[0011] Furthermore, step S3 includes the following sub-steps: Step S31, analyze the access duration of any page within the platform in the first standard behavior data of the user; If the access duration of any page within the platform accessed by the user is less than or equal to the first access duration threshold, determine that the page is a page with abnormal access duration; If the access duration of any page within the platform accessed by the user is greater than the first access duration threshold and less than or equal to the second access duration threshold, determine that the page is a page with normal access duration, and no operation is performed; If the access duration of any page within the platform accessed by the user is greater than the second access duration threshold and less than or equal to the third access duration threshold, record the page as a page with high access duration, and record the access duration of the page; If the access duration of any page within the platform accessed by the user is greater than the third access duration threshold, determine that the page is a page with abnormal access duration; where the third access duration threshold is greater than the second access duration threshold, the second access duration threshold is greater than the first access duration threshold, and the first access duration threshold is greater than zero; Step S32, analyze the trigger frequency of any button on the page in the first standard behavior data of the user; If the trigger frequency of any button on the page is less than or equal to the first trigger frequency threshold, determine that the button is a button with abnormal trigger frequency; If the trigger frequency of any button on the page is greater than the first trigger frequency threshold and less than or equal to the second trigger frequency threshold, determine that the button is a button with normal trigger frequency, and no operation is performed; If the triggering frequency of any button on the page is greater than the second triggering frequency threshold and less than or equal to the third triggering frequency threshold, mark this button as a high-frequency triggering button and obtain its triggering frequency; If the triggering frequency of any button on the page is greater than the third triggering frequency threshold, determine that this button is a button with an abnormal triggering frequency; where the third triggering frequency threshold is less than one, at the same time the third triggering frequency threshold is greater than the second triggering frequency threshold, the second triggering frequency threshold is greater than the first triggering frequency threshold, and the first triggering frequency threshold is greater than zero.

[0012] Furthermore, step S3 further includes the following sub-steps: Step S33, combine the page with abnormal access duration and the button with abnormal triggering frequency into the data to be optimized on the platform; Step S34, combine the page with high access duration on the platform and the high-frequency triggering button into the personalized recommendation data on the platform; Step S35, optimize the platform based on the personalized recommendation data and the data to be optimized on the platform.

[0013] Furthermore, step S4 includes the following sub-steps: Step S41, clean the second behavior data of the user according to steps S2 - S3, and analyze the second standard behavior data obtained from the cleaning. Analyze to obtain the triggering frequency CFP of the high-frequency triggering button in any page, and the access duration FWS of the page connected by the user accessing this button; Step S42, calculate the attention score GZF of the user through the formula. The specific formula is as follows: GZF = v1×CFP + v2×FWS (algorithm), where v1 and v2 are weight coefficients with fixed values, and v1 > v2.

[0014] Furthermore, step S4 further includes the following sub-steps: Step S43, analyze the first active user number in the first standard behavior data of the user and the second active user number in the second standard behavior data, and analyze to obtain the active user growth value of the platform, specifically: Obtain the first active user number DYH in the first standard behavior data and the second active user number DEH in the second standard behavior data, and calculate the active user growth index ZZZ of the platform through the formula ZZZ = (DEH + 1) / (DYH + 1); Step S44, calculate the satisfaction score of the user through the formula MYF = w1×GZF + w2×ZZZ; where w1 and w2 are weight coefficients with fixed values, and w1 > w2; If the satisfaction score of the user is less than or equal to the satisfaction score threshold, repeat step S3 to optimize the platform; If the user's satisfaction score is greater than the satisfaction score threshold, no operation is performed.

[0015] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are as follows: The present invention first obtains the first behavior data of the user when accessing the platform, then formulates data cleaning rules, cleans the first behavior data of the user, and obtains the first standard behavior data of the user after cleaning. Then, the first standard behavior data of the user is analyzed, and the platform is optimized according to the analysis results. Finally, the second behavior data during the user's access to the platform after optimization is obtained, and the second behavior data is analyzed to obtain the user's satisfaction score. The present invention realizes the optimization of the acquired data by unifying the data tracking rules. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 is the overall system block diagram of the present invention; Figure 2 is the flowchart for obtaining data required for platform optimization in the present invention; Figure 3 is the structural schematic diagram of the computing device in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1: Please refer to Figure 1 and Figure 2 As shown, the technical solution provided by the present invention is: a one-network-one-office data tracking service method based on multiple terminals. This method is mainly used to unify the data tracking rules, obtain platform data based on the unified data tracking rules, and optimize the platform according to the platform data. The method is as follows: Step S1, obtain the first behavior data of the user when accessing the platform; In this embodiment, the platform is specifically a one-network-one-office platform; Specifically, during the platform development process, the platform is divided into front-end development and back-end development. In front-end development, front-end data tracking can be completed by introducing a set of general JavaScript SDK; in back-end development, back-end data tracking can be completed by introducing a set of Java-based back-end data tracking SDK; in special cases, the first behavior data can be directly collected by calling the data tracking API. Exemplarily, on the PC side, by means of JavaScript data embedding in the browser, behaviors such as user clicks or page views on the platform are recorded; On the mobile side, by using the data embedding tools integrated in the SDK, behaviors such as user clicks, page views, and jumps on the platform are recorded; It should be specifically noted that the data embedding tools integrated in the SDK include Firebase, Amplitude, etc.; In this embodiment, the step S1 includes the following sub-steps: Step S11, create the trigger rules for page view events and click events within the platform; Specifically, when any one of the fields "event_type='page_view'", "page_url", and "referrer_url" exists in the event fields of the data embedding event, the data embedding event is recorded as a page view event, and the corresponding page view timestamp when the page view event is triggered is recorded; when any one of the fields "event_type='click'", "element_id", and "page_url" exists in the event fields of the data embedding event, the data embedding event is recorded as a click event, indicating that the user triggers the click event by clicking a button within the page; Furthermore, a data embedding event refers to pre-setting some codes or marks in an application, website, or system to collect user behavior data and operation records for analyzing user behavior patterns, product usage, and system operation status, etc.; Step S12, record the page view timestamp when the user first accesses any page within the platform as the first page view timestamp, record the timestamp when the user jumps to other pages within the platform or exits the platform page as the second page view timestamp, and subtract the first page view timestamp from the second page view timestamp to obtain the access duration of the user accessing any page within the platform; Among them, the second page view timestamp must be later than the first page view timestamp; Step S13, when all users trigger click events within any page, obtain the trigger times of each button, and then obtain the total trigger times of all users triggering click events within this page, and divide the trigger times of the button by the total trigger times to obtain the trigger frequency of this button; Specifically, a button is an interactive element that the user can click within the page. When the user clicks the button, the click event of the page where the button is located is triggered; Step S14, obtain the number of first active users within the past week in the platform and record the user IDs of all users; Specifically, obtaining the number of first active users and user IDs within the past week in the platform is prior art; Step S15: Record the access duration of the user accessing any page within the platform, the triggering frequency of any button within the page, and the number of first active users as the first behavior data of the user.

[0020] Step S2: Develop data cleaning rules to clean the first behavior data of the user, and obtain the first standard behavior data of the user after cleaning. In this embodiment, step S2 includes the following sub-steps: Step S21: Develop the access duration rule for the user. If the access duration of the user accessing any page within the platform is less than or equal to the duration threshold, it is determined that the user has made a misoperation or there is a page loading error, and the access duration less than or equal to the duration threshold is excluded. If the access duration of the user accessing any page within the platform is greater than the duration threshold, no operation is performed. Step S22: Develop the triggering frequency rule for any button within the page, and capture the abnormal triggering frequency of the button according to the triggering frequency rule. Among them, if the triggering frequency of any button within the page is less than zero or greater than one, it is determined that the triggering frequency of the corresponding button is abnormal, and the abnormal triggering frequency is recorded. If the triggering frequency of any button within the page is greater than or equal to zero and less than or equal to one, no operation is performed. Step S23: Develop the user activity rule, and optimize the platform according to the user activity rule. Further, when the number of first active users within the past week in the platform is less than zero, or the number of first active users within the past week in the platform is not an integer, optimize the platform, re-obtain the user ID, and record the number of active user IDs within the past week as the number of first active users. Actually, the page display content of the platform can be changed, and each function in the platform can be optimized to achieve convenient access. When the number of first active users within the past week in the platform is greater than or equal to zero and the number of first active users is an integer, no operation is performed. Step S24: Record the access duration rule of the user, the triggering frequency rule of any button within the page, and the user activity rule as data cleaning rules. Step S25: Clean the first behavior data of the user according to the data cleaning rules, and obtain the first standard behavior data of the user after cleaning. Among them, the cleaning of the user's first-line behavior data is specifically as follows: According to the data cleaning rules, the user's first-line behavior data is screened. The first-line behavior data that conforms to the data cleaning rules is retained, and the first-line behavior data that does not conform to the data cleaning rules is processed according to the data cleaning rules; Step S3, analyze the user's first standard behavior data, and optimize the platform according to the analysis results; In this embodiment, the step S3 includes the following sub-steps: Step S31, analyze the access duration of any page in the platform in the user's first standard behavior data; If the access duration of any page in the platform accessed by the user is less than or equal to the first access duration threshold, it is determined that the page is a page with abnormal access duration; If the access duration of any page in the platform accessed by the user is greater than the first access duration threshold and less than or equal to the second access duration threshold, it is determined that the page is a page with normal access duration, and no operation is performed; If the access duration of any page in the platform accessed by the user is greater than the second access duration threshold and less than or equal to the third access duration threshold, mark the page as a page with high access duration, and record the access duration of the page; If the access duration of any page in the platform accessed by the user is greater than the third access duration threshold, it is determined that the page is a page with abnormal access duration; Among them, the third access duration threshold is greater than the second access duration threshold, the second access duration threshold is greater than the first access duration threshold, and the first access duration threshold is greater than zero; Specifically, the obtaining process of the first access duration threshold is as follows: Obtain the access duration of any page in the platform accessed by the user, and add up the access durations of all pages in the platform accessed by all users to obtain the total access duration of all pages in the platform accessed by all users. Then obtain the number of first active users, divide the total access duration by the number of first active users to obtain the average access duration of each user accessing the pages in the platform, and use the average access duration as the first access duration threshold; Step S32, analyze the trigger frequency of any button on the page in the user's first standard behavior data; If the trigger frequency of any button on the page is less than or equal to the first trigger frequency threshold, it is determined that the button is a button with abnormal trigger frequency; If the trigger frequency of any button on the page is greater than the first trigger frequency threshold and less than or equal to the second trigger frequency threshold, it is determined that the button is a button with normal trigger frequency, and no operation is performed; If the trigger frequency of any button on the page is greater than the second trigger frequency threshold and less than or equal to the third trigger frequency threshold, mark the button as a high-frequency trigger button, and obtain the trigger frequency of the button; If the triggering frequency of any button on the page is greater than the third triggering frequency threshold, determine that button as a button with an abnormal triggering frequency; Wherein, the third triggering frequency threshold is less than one, and at the same time, the third triggering frequency threshold is greater than the second triggering frequency threshold, the second triggering frequency threshold is greater than the first triggering frequency threshold, and the first triggering frequency threshold is greater than zero; Step S33, combine the page with an abnormal access duration and the button with an abnormal triggering frequency into the data to be optimized on the platform; Step S34, combine the page with a high access duration on the platform and the button with a high-frequency trigger into the personalized recommendation data on the platform; Step S35, optimize the platform according to the personalized recommendation data and the data to be optimized on the platform; Step S4, obtain the second behavior data during the user's access to the platform after optimization, and analyze the second behavior data to obtain the user's satisfaction score; Specifically, the user's second behavior data is the same as the first behavior data; In this embodiment, the step S4 includes the following sub-steps: Step S41, as described in steps S2 - S3, clean the user's second behavior data, and analyze the second standard behavior data obtained after cleaning to obtain the triggering frequency CFP of the high-frequency trigger button in any page, and the access duration FWS of the user accessing the page connected to the button; Step S42, calculate the user's attention score GZF through the formula. The specific formula is as follows: GZF = v1×CFP + v2×FWS, where v1 and v2 are weight coefficients with fixed values, and v1 > v2; Step S43, analyze the first active user number in the user's first standard behavior data and the second active user number in the second standard behavior data to obtain the active user growth value of the platform; Specifically, obtain the first active user number DYH in the first standard behavior data and the second active user number DEH in the second standard behavior data, and calculate the active user growth index ZZZ of the platform through the formula. The specific formula is as follows: ZZZ = (DEH + 1) / (DYH + 1); Step S44, calculate the user's satisfaction score through the formula MYF = w1×GZF + w2×ZZZ. In the formula, w1 and w2 are weight coefficients with fixed values, and w1 > w2; If the user's satisfaction score is less than or equal to the satisfaction score threshold, determine that the user is not satisfied with the optimization of the platform, and repeat the optimization of the platform as described in step S3; If the user's satisfaction score is greater than the satisfaction score threshold, it is determined that the user is satisfied with the platform optimization, and no operation is performed.

[0021] In this application, if there are corresponding calculation formulas, the above calculation formulas are all dimensionless and take their numerical values for calculation. For coefficients such as weight coefficients and proportionality coefficients in the formulas, the sizes set are for obtaining a result value by quantifying each parameter. Regarding the sizes of the weight coefficients and proportionality coefficients, as long as the proportional relationship between the parameters and the result value is not affected.

[0022] Embodiment 2: Figure 3 An example of a schematic structural diagram of a computer device is shown. The computer device may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The processor can call the logical instructions in the memory to execute a one-network-one-office data tracking service method based on multiple terminals. The method includes: obtaining the first behavior data when the user accesses the platform; formulating data cleaning rules to clean the user's first behavior data, and obtaining the user's first standard behavior data after cleaning; analyzing the user's first standard behavior data and optimizing the platform according to the analysis results; obtaining the second behavior data during the user's access to the platform after optimization, and analyzing the second behavior data to obtain the user's satisfaction score.

[0023] In addition, when the above logical instructions in the memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0024] On the other hand, the present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a multi-terminal one-network-one-service data logging service method provided by the above-mentioned various methods. The method includes: obtaining first behavior data when a user accesses a platform; formulating data cleaning rules to clean the first behavior data of the user, and obtaining first standard behavior data of the user after cleaning; analyzing the first standard behavior data of the user, and optimizing the platform according to the analysis result; obtaining second behavior data during the process of the user accessing the optimized platform, and analyzing the second behavior data to obtain a satisfaction score of the user.

[0025] In another aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is configured to execute a multi-terminal one-network-one-service data logging service method provided by the above-mentioned various methods. The method includes: obtaining first behavior data when a user accesses a platform; formulating data cleaning rules to clean the first behavior data of the user, and obtaining first standard behavior data of the user after cleaning; analyzing the first standard behavior data of the user, and optimizing the platform according to the analysis result; obtaining second behavior data during the process of the user accessing the optimized platform, and analyzing the second behavior data to obtain a satisfaction score of the user.

[0026] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative efforts.

[0027] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, also by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0028] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-terminal-based one-network-one-service data tracking service method, characterized in that, The method is as follows: Step S1, obtain the first behavior data when the user accesses the platform; Step S2, formulate data cleaning rules, clean the first behavior data of the user, and obtain the first standard behavior data of the user after cleaning; Step S3, analyze the first standard behavior data of the user, and optimize the platform according to the analysis results; Step S4, obtain the second behavior data during the user's access to the platform after optimization, and analyze the second behavior data to obtain the user's satisfaction score.

2. The method for one-network-through handling buried point service based on multiple terminals according to claim 1, wherein, The said Step S1 includes the following sub-steps: Step S11, create the trigger rules for page view events and click events within the platform; Step S12, record the page view timestamp of the page view event triggered when the user first accesses any page within the platform as the first page view timestamp, and record the timestamp when the user jumps to other pages within the platform or exits the platform page as the second page view timestamp. Subtract the first page view timestamp from the second page view timestamp to obtain the access duration of the user accessing any page within the platform; Step S13, when all users trigger click events within any page, obtain the trigger times of each button, and then obtain the total trigger times of all users triggering click events within this page. Divide the trigger times of the button by the total trigger times to obtain the trigger frequency of this button; Step S14, obtain the number of first active users within the platform in the past week, and record the user IDs of all users; Step S15, record the access duration of the user accessing any page within the platform, the trigger frequency of any button within the page, and the number of first active users as the first behavior data of the user.

3. A method for one-network-one-service data tracking service based on multiple terminals according to claim 2, characterized in that When any of the fields "event_type='page_view'", "page_url", and "referrer_url" exists in the event fields of the buried point event, record this buried point event as a page view event, and record the corresponding page view timestamp when the page view event is triggered; When any of the fields "event_type='click'", "element_id", and "page_url" exists in the event fields of the buried point event, record this buried point event as a click event, indicating that the user triggers the click event by clicking a button within the page.

4. A multi-terminal-based one-network-one-service buried point service method according to claim 1, characterized in that The said Step S2 includes the following sub-steps: Step S21, formulate the access duration rule for the user, specifically: If the access duration of the user accessing any page within the platform is less than or equal to the duration threshold, it is determined that the user has made a misoperation or the page has a loading error, and the access duration less than or equal to the duration threshold is excluded; If the access duration of the user accessing any page within the platform is greater than the duration threshold, no operation is performed; Step S22, formulate the trigger frequency rule for any button within the page, and capture the abnormal trigger frequency of the button according to the trigger frequency rule, specifically: If the trigger frequency of any button within the page is less than zero or greater than one, it is determined that the trigger frequency of the corresponding button is abnormal, and the abnormal trigger frequency is recorded; If the trigger frequency of any button within the page is greater than or equal to zero and less than or equal to one, no operation is performed.

5. A one-network-one-service buried point service method based on multiple terminals according to claim 4, characterized in that The said Step S2 also includes the following sub-steps: Step S23, formulate user activity rules and optimize the platform according to the user activity rules, specifically as follows: When the number of first active users in the past week on the platform is less than zero, or the number of first active users in the past week on the platform is not an integer, optimize the platform, re-obtain the user ID, and record the number of active user IDs in the past week as the number of first active users; Step S24, record the user's access duration rule, the trigger frequency rule of any button on the page, and the user activity rule as data cleaning rules; Step S25, clean the user's first behavior data according to the data cleaning rules to obtain the user's first standard behavior data.

6. The method for one-network-one-service data logging service based on multiple terminals according to claim 1, wherein The said Step S3 includes the following sub-steps: Step S31, analyze the access duration of any page within the platform in the user's first standard behavior data; If the access duration of any page within the platform by the user is less than or equal to the first access duration threshold, determine that page as a page with abnormal access duration; If the access duration of any page within the platform by the user is greater than the first access duration threshold and less than or equal to the second access duration threshold, determine that page as a page with normal access duration and do not perform any operation; If the access duration of any page within the platform by the user is greater than the second access duration threshold and less than or equal to the third access duration threshold, mark that page as a page with high access duration and record the access duration of that page; If the access duration of any page within the platform by the user is greater than the third access duration threshold, determine that page as a page with abnormal access duration; where the third access duration threshold is greater than the second access duration threshold, the second access duration threshold is greater than the first access duration threshold, and the first access duration threshold is greater than zero; Step S32, analyze the trigger frequency of any button on the page in the user's first standard behavior data; If the trigger frequency of any button on the page is less than or equal to the first trigger frequency threshold, determine that button as a button with abnormal trigger frequency; If the trigger frequency of any button on the page is greater than the first trigger frequency threshold and less than or equal to the second trigger frequency threshold, determine that button as a button with normal trigger frequency and do not perform any operation; If the trigger frequency of any button on the page is greater than the second trigger frequency threshold and less than or equal to the third trigger frequency threshold, mark that button as a high-frequency trigger button and obtain the trigger frequency of that button; If the trigger frequency of any button on the page is greater than the third trigger frequency threshold, determine that button as a button with abnormal trigger frequency; where the third trigger frequency threshold is less than one, at the same time the third trigger frequency threshold is greater than the second trigger frequency threshold, the second trigger frequency threshold is greater than the first trigger frequency threshold, and the first trigger frequency threshold is greater than zero.

7. A method for one - network - one - stop service data tracking based on multiple terminals according to claim 5, characterized in that, The said Step S3 also includes the following sub-steps: Step S33, combine the pages with abnormal access duration and the buttons with abnormal trigger frequency into the data to be optimized for the platform; Step S34, combine the pages with high access duration on the platform and the high-frequency trigger buttons into the personalized recommendation data for the platform; Step S35, optimize the platform according to the personalized recommendation data and the data to be optimized for the platform.

8. A method for one-network-one-service data tracking service based on multiple terminals according to claim 1, wherein The said Step S4 includes the following sub-steps: Step S41: Clean the second behavior data of the user according to Steps S2 - S3, and analyze the second standard behavior data obtained after cleaning to obtain the trigger frequency CFP of the high-frequency trigger buttons within any page and the access duration FWS of the user to the page connected by the button. Step S42: Calculate the user's attention score GZF through the formula. The specific formula is as follows: GZF = v1×CFP + v2×FWS (algorithm), where v1 and v2 are weight coefficients with fixed values, and v1 > v2.

9. The method for one-network-one-service data tracking service based on multiple terminals according to claim 8, wherein Step S4 further includes the following sub-steps: Step S43: Analyze the first active user number in the first standard behavior data of the user and the second active user number in the second standard behavior data to obtain the active user growth value of the platform. Specifically: Obtain the first active user number DYH in the first standard behavior data and the second active user number DEH in the second standard behavior data, and calculate the active user growth index ZZZ of the platform through the formula ZZZ = (DEH + 1) / (DYH + 1). Step S44: Calculate the user's satisfaction score through the formula MYF = w1×GZF + w2×ZZZ; where w1 and w2 are weight coefficients with fixed values, and w1 > w2; If the user's satisfaction score is less than or equal to the satisfaction score threshold, repeat Step S3 to optimize the platform; If the user's satisfaction score is greater than the satisfaction score threshold, do not perform any operation.

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