Dynamic data statistical method and system adopting multi-dimensional data annotation

Abstract

The invention discloses a dynamic data statistical system adopting multi-dimensional data annotation, and relates to the technical field of data statistics, the dynamic data statistical system comprises a data acquisition module, a data annotation module, a data storage and management module, a data analysis model and a data statistical module which run in sequence, the data acquisition module is used for acquiring dynamic data of a website; the technical key points are as follows: assigning dynamic data subjected to label extraction, and generating an activeness evaluation value Aev of a corresponding plate according to an assigned data set, so that the complexity of data processing can be reduced to improve the calculation effect, data units can be automatically unified, the preprocessing operation on the data set is avoided, and the data processing efficiency is improved. In order to ensure effectiveness and accuracy of follow-up evaluation on the activeness of each plate, an activeness stability coefficient Asct is generated according to an activeness evaluation value Aev in a preset time period T, then direct observation is performed in a chart form, and the effect of data statistical work is reflected.

Description

Technical Field

[0001] This invention relates to the field of data statistics technology, specifically to a dynamic data statistics method and system employing multidimensional data annotation. Background Technology

[0002] Data statistics techniques refer to a series of technical methods for collecting, organizing, analyzing, and interpreting data using statistical principles and methods. It includes the following specific components: Data collection: collecting relevant data through sampling, observation, experimentation, etc.; Data organization: organizing, cleaning, and preprocessing the collected data to ensure its accuracy and usability; Descriptive statistics: summarizing and describing the data, including calculating the central tendency (e.g., mean, median), dispersion (e.g., standard deviation, range), and distribution pattern (e.g., histogram, box plot); Exploratory data analysis: exploring the relationships and trends between data and discovering patterns and characteristics through methods such as creating charts and calculating correlation coefficients; Statistical inference: using sample data to infer about the population, including estimating population parameters and testing hypotheses; Regression analysis: establishing mathematical models to explore the relationship between independent and dependent variables, and making predictions and interpretations; Hypothesis testing: using statistical methods to determine whether sample data supports a certain hypothesis, used for decision-making on practical problems or to verify scientific theories; Data visualization: visually displaying data through charts, images, maps, etc., helping people better understand the meaning and trends of the data.

[0003] The existing patent, CN104869009B, entitled "System and Method for Website Data Statistics," specifies a technical solution for a system and method for website data statistics. This solution includes: a method for statistically analyzing basic website traffic data to understand website traffic trends and gain insights into visitor behavior. This includes: adding data statistics code and setting data transmission code to website pages to determine, create, record, and transmit basic information about user website visits; deploying a backend processing program to perform program analysis, send data, and group the data; dividing the data into multiple table files as needed for recording; and data visualization using chart components and a server-side architecture. While this system consumes little hardware, is separated from the production environment, and has good scalability, it cannot perform detailed statistics and evaluation of dynamic data.

[0004] Combining the aforementioned patents and existing technologies, a large amount of dynamic data is generated during user visits to various sections of a website, such as online time and number of online users. Typically, when evaluating each section, it is necessary to collect and process this dynamic data, and calculate the activity level of the corresponding section based on the processing results. In specific scenarios, traditional server-based data statistics systems may face the problem of insufficient processing speed, resulting in the data statistics and activity evaluation results not reflecting the actual changes in a timely manner, thus affecting the actual usability of the statistics system. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a dynamic data statistics method and system employing multi-dimensional data annotation. The method assigns values ​​to the extracted and annotated dynamic data, generating an activity evaluation value (Aev) for each corresponding section based on the assigned dataset. This not only reduces the complexity of data processing and improves computational efficiency but also automatically standardizes data units, avoiding preprocessing of the dataset. To ensure the effectiveness and accuracy of subsequent activity evaluations of each section, an activity stability coefficient (Asct) is generated based on the activity evaluation value (Aev) within a preset time period T. This coefficient is then directly observed through charts, demonstrating the effectiveness of the data statistics work and solving the problems raised in the background technology.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A dynamic data statistics system employing multidimensional data annotation, the system comprising:

[0010] The data acquisition module obtains dynamic data from the website;

[0011] The data annotation module establishes a tag engine and uses this engine to annotate the acquired dynamic data.

[0012] The data storage module stores both dynamic data and labeled dynamic data in the database and builds an index based on a distributed search engine.

[0013] The data analysis model builds a rule engine to assign values ​​to the extracted and labeled dynamic data. When the data is determined to be highly dynamic, it is assigned a value of n, and when it is determined to be low dynamic data, it is assigned a value of n-1. The data analysis model is constructed to generate the corresponding activity evaluation value Aev based on the assigned datasets under the conditions of different sections within the website.

[0014] The data statistics module builds a data calculation model, generates an activity stability coefficient Asct for the corresponding sector based on several activity evaluation values ​​Aev obtained within a preset time period T, sorts the activity stability coefficients Asct generated for each sector from largest to smallest, and displays the sorting results in the form of a chart.

[0015] Furthermore, dynamic data includes: website visits, online users and online time for each section of the website, interaction data for each section of the website, and bounce rate. Website visits represent the total number of times the website is visited in a day. Online users for each section represent the number of users online for each section in a day. Online time for each section represents the total time spent on the corresponding section's page. Interaction data includes the number of comments, likes, and shares. Bounce rate represents the proportion of users who leave the page after visiting the corresponding section without further interaction.

[0016] Furthermore, the specific process of labeling the acquired dynamic data is as follows: acquire the website visits within a day, the number of online users and online time in each section of the website, the interaction data and rebound rate in each section of the website, compare different dynamic data with the corresponding standard thresholds, and label the dynamic data that does not exceed the standard threshold and the dynamic data that exceeds the standard threshold as high dynamic data or low dynamic data respectively.

[0017] Furthermore, the specific steps performed in the data storage module are as follows:

[0018] Data storage: Store dynamic data and annotated dynamic data in the selected database;

[0019] Data synchronization: Synchronizing data stored in the database to the distributed search engine;

[0020] Creating an index: Creating an index for the dataset to be queried. In a distributed search engine, the index creation process involves sharding the data and storing it on different nodes.

[0021] Data retrieval: Using the search engine's query syntax and API, relevant data is retrieved from the distributed search engine by searching and filtering based on indexed fields and query conditions.

[0022] Furthermore, the assignment dataset includes assignment A, assignment B, assignment C, assignment D, and assignment E.

[0023] Furthermore, the formula used to generate the corresponding sector's activity assessment value (Aev) is as follows:

[0024]

[0025] In the formula, a1, a2, a3, a4, and a5 are the preset proportional coefficients for assignment A, assignment B, assignment C, assignment D, and assignment E, respectively, and a1 > a5 > a4 > a2 > a3 > 0, a1 + a2 + a3 + a4 + a5 = 4.75, G is the constant correction coefficient, and e is the natural constant.

[0026] Furthermore, the process of generating the activity stability coefficient Asct for the corresponding sector is as follows:

[0027] First, calculate the average of several activity assessment values ​​(Aev) for the corresponding sectors obtained within a preset time period T, i.e. Then, the activity stability coefficient Asct is generated based on the following formula:

[0028]

[0029] In the formula, i = {1, 2, 3, ..., s}, s represents the number of activity assessment values ​​(Aev), and s is a positive integer. i This represents different activity evaluation values ​​among several activity evaluation values ​​(Aev).

[0030] A dynamic data statistics method employing multidimensional data annotation includes the following steps:

[0031] S1. Obtain dynamic data from the website;

[0032] The dynamic data includes: website visits, online users and online time for each section of the website, interaction data for each section of the website, and bounce rate. Website visits represent the total number of times the website is visited in a day. Online users for each section represent the number of users online for each section in a day. Online time for each section represents the total time spent on the corresponding section's page. Interaction data includes the number of comments, likes, and shares. Bounce rate represents the proportion of users who leave the page after visiting the corresponding section without continuing to interact with it.

[0033] S2. Establish a tagging engine and label the acquired dynamic data based on the tagging engine;

[0034] The specific process of annotating the acquired dynamic data is as follows: acquire the website visits, online users and online time of each section of the website, interaction data and rebound rate of each section of the website within a day, compare different dynamic data with the corresponding standard thresholds, and annotate dynamic data that does not exceed the standard threshold and dynamic data that exceeds the standard threshold separately as high dynamic data or low dynamic data.

[0035] S3. Both dynamic data and labeled dynamic data are stored in the database, and an index is built based on a distributed search engine; the specific steps in the data storage module are as follows:

[0036] Data storage: Store dynamic data and annotated dynamic data in the selected database;

[0037] Data synchronization: Synchronizing data stored in the database to the distributed search engine;

[0038] Creating an index: Creating an index for the dataset to be queried. In a distributed search engine, the index creation process involves sharding the data and storing it on different nodes.

[0039] Data retrieval: Using the query syntax and API of the search engine, relevant data is retrieved from the distributed search engine by searching and filtering based on indexed fields and query conditions;

[0040] S4. Build a rule engine to assign values ​​to the extracted and labeled dynamic data. If the data is determined to be high dynamic data, assign it a value of n; if it is determined to be low dynamic data, assign it a value of n-1. Build a data analysis model to generate the corresponding activity evaluation value Aev based on the assigned datasets (including assignment A, assignment B, assignment C, assignment D, and assignment E) for different sections of the website.

[0041] S5. Build a data calculation model. Based on the activity evaluation values ​​Aev of the corresponding sectors obtained within the preset time period T, generate the activity stability coefficient Asct of the corresponding sectors. Sort the activity stability coefficients Asct generated by each sector from largest to smallest and display the sorting results in the form of a chart.

[0042] (III) Beneficial Effects

[0043] This invention provides a dynamic data statistics method and system using multidimensional data annotation, which has the following beneficial effects:

[0044] 1. This invention establishes a tag engine, annotates the acquired dynamic data based on the tag engine, and compares different dynamic data with corresponding standard thresholds. This allows for a quick and effective determination of the high and low results of the corresponding dynamic data. Based on these results, the activity level of the corresponding section on the website can be preliminarily determined. Both the high and low results and the dynamic data are stored in a database for subsequent searching and analysis.

[0045] 2. This invention assigns values ​​to the extracted and labeled dynamic data, and generates an activity evaluation value Aev for the corresponding section based on the assigned dataset. This not only reduces the complexity of data processing and improves computational efficiency, but also automatically unifies data units, avoiding preprocessing of the dataset. To ensure the effectiveness and accuracy of subsequent evaluation of the activity of each section, an activity stability coefficient Asct is generated based on the activity evaluation value Aev within a preset time period T. This coefficient is then directly observed through charts, demonstrating the effectiveness of data statistics and improving server operating efficiency to some extent. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the modular structure of a dynamic data statistics system using multidimensional data annotation according to the present invention;

[0047] Figure 2 This is a flowchart illustrating the specific steps of a dynamic data statistics method using multidimensional data annotation according to the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1:

[0050] Please see Figure 1 This embodiment provides a dynamic data statistics system using multidimensional data annotation. The application scenario of this system is to perform dynamic data statistics on a built website. The entire statistics system includes a data acquisition module, a data annotation module, a data storage module, a data analysis model, and a data statistics module that run sequentially.

[0051] The data acquisition module acquires dynamic data from the website.

[0052] Dynamic data includes: website visits, online users and online time for each section of the website, interaction data and rebound rate for each section of the website. The aforementioned sections include at least game, music, dance, technology and lifestyle sections, such as various entertainment websites. Depending on the actual website, the content and number of its corresponding sections will vary. In this application, the subsequent data statistics are based on the aforementioned game, music, dance, technology and lifestyle sections.

[0053] Website traffic: The total number of times a website is visited within a specific time period, such as a day. Traffic data is obtained through website server logs and website analytics tools, such as Google Analytics.

[0054] Online users for each section: refers to the number of users online in each section within a specific time period, i.e., within one day. Online user data is obtained by monitoring website access in real time or by using website analytics tools.

[0055] Online time for each section: This refers to the total time users spend on a specific section's page. It is achieved by tracking user page dwell time through website analytics tools or custom code and calculating the online time for each section.

[0056] Interaction data: This includes the number of comments, likes, and shares, referring to the number of user interactions on a specific section. Interaction data is collected through social media sharing buttons and user behavior analysis tools.

[0057] Rebound rate: refers to the proportion of users who leave a webpage without further interaction after visiting it, i.e., the proportion of visits lasting no more than 3 seconds out of the total number of visits. Rebound rate data can be obtained through website analytics tools or calculated based on users' page browsing behavior in access logs.

[0058] In summary, the methods for obtaining this dynamic data can be chosen based on specific circumstances, such as website analytics tools, real-time monitoring tools, custom code, and log analysis methods. These tools and technologies can help collect, monitor, and analyze website activity data and provide relevant metrics.

[0059] The data annotation module establishes a tag engine and uses this engine to annotate the acquired dynamic data.

[0060] The tagging engine labels each piece of dynamic data based on its characteristics to achieve classification.

[0061] The specific process of labeling the acquired dynamic data is as follows:

[0062] Obtain website traffic, online users and online time for each section of the website, interaction data and rebound rate for each section of the website within a specific time period, i.e., within one day; compare different dynamic data with corresponding standard thresholds, and label dynamic data that do not exceed the standard thresholds and dynamic data that exceed the standard thresholds respectively;

[0063] For example, when comparing website traffic with the corresponding labeling threshold, if the website traffic is less than or equal to the corresponding standard threshold, it is labeled as low website traffic; if the website traffic is greater than the corresponding standard threshold, it is labeled as high website traffic.

[0064] The number of online users in a certain section of the website is compared with the corresponding standard threshold. If the number of online users in a certain section of the website is less than or equal to the corresponding standard threshold, it is marked as low number of online users. If the number of online users in a certain section of the website is greater than the corresponding standard threshold, it is marked as high number of online users.

[0065] The online duration of a certain section within the website is compared with the corresponding labeling threshold. If the online duration of a certain section within the website is less than or equal to the corresponding standard threshold, it is labeled as low online duration; if the online duration of a certain section within the website is greater than the corresponding standard threshold, it is labeled as high online duration.

[0066] The interaction data of a certain section of the website is compared with the corresponding labeling threshold. If the interaction data of a certain section of the website is less than or equal to the corresponding standard threshold, it is labeled as low interaction data. If the interaction data of a certain section of the website is greater than or equal to the corresponding standard threshold, it is labeled as high interaction data.

[0067] The rebound rate of a certain section of the website is compared with the corresponding standard threshold. If the rebound rate of a certain section of the website is less than or equal to the corresponding standard threshold, it is marked as a low rebound rate. If the rebound rate of a certain section of the website is greater than the corresponding standard threshold, it is marked as a high rebound rate.

[0068] It should be noted that the standard thresholds mentioned above are different for each different dynamic data. Each standard threshold is set based on historical data and can be adjusted accordingly as needed.

[0069] The data storage module stores both dynamic data and labeled dynamic data in the database and builds an index based on a distributed search engine to support fast subsequent data queries.

[0070] When storing dynamic data and labeled dynamic data in a database, a database system suitable for large-scale data storage and querying can be selected, such as relational databases (e.g., MySQL, PostgreSQL), non-relational databases (e.g., MongoDB, Cassandra), or distributed databases (e.g., Hadoop HBase, Apache Cassandra). In this application, the distributed database Hadoop HBase is specifically used.

[0071] For index building in distributed search engines, common open-source distributed search engines include Elasticsearch and Apache Solr. This application uses Elasticsearch, which provides powerful search and indexing capabilities and supports data sharding, distributed storage, and horizontal scaling.

[0072] The specific steps performed in the data storage module are as follows:

[0073] Data storage: Store dynamic data and labeled dynamic data in the selected database, design corresponding data tables or document formats according to the data structure, and optimize data storage and query efficiency through appropriate data models and indexing strategies;

[0074] Data synchronization: Synchronize data stored in the database to the distributed search engine. Data synchronization tools can be used to periodically import data from the database to the search engine.

[0075] Creating an index: Create an index for the dataset to be queried. In a distributed search engine, the index creation process will shard the data and store it on different nodes to achieve distributed data storage and query acceleration. During the index creation process, you need to specify configuration parameters such as the type of the index field, the analyzer, and the storage method.

[0076] Data retrieval: Using the search engine's query syntax and API, relevant data can be quickly retrieved from the distributed search engine by searching and filtering based on indexed fields and query conditions;

[0077] It is important to note that the specific database selection and indexing methods for distributed search engines will vary depending on factors such as specific business needs, data size, and access patterns. Appropriate technologies and tools can be selected based on the project scale and performance requirements.

[0078] Specifically, by establishing a tagging engine, the acquired dynamic data is labeled based on the tagging engine, and different dynamic data are compared with the corresponding standard thresholds. This allows for a quick and effective determination of the high and low results of the corresponding dynamic data. Based on these results, the activity level of the corresponding sections on the website can be preliminarily determined. Both the high and low results and the dynamic data are stored in the database for subsequent searching and analysis.

[0079] The data analysis model and rule engine are built to assign values ​​to the extracted and labeled dynamic data. Data deemed high-dynamic is assigned a value of n, while data deemed low-dynamic is assigned a value of n-1. The data analysis model is constructed to address different sections of the website. Based on the assigned datasets (which include values ​​A, B, C, D, and E), it generates the corresponding activity evaluation value Aev for each section. For example, in the website's gaming section, if within a day there are high website visits, low online users, high online time, low interaction data, and high bounce rate, then in the rule engine, the assigned values ​​A, B, C, D, and E in the dataset would be n, n-1, n, n-1, and n, respectively.

[0080] The formula used to generate the activity assessment value (Aev) for the corresponding sector is as follows:

[0081]

[0082] In the formula, a1, a2, a3, a4, and a5 are the preset proportional coefficients for assignment A, assignment B, assignment C, assignment D, and assignment E, respectively, and a1 > a5 > a4 > a2 > a3 > 0, a1 + a2 + a3 + a4 + a5 = 4.75, G is a constant correction coefficient, the specific value of which can be adjusted by the user or generated by the analysis function fitting, and the value range of G is 0.56 to 1.07, and e is the natural constant;

[0083] It should be noted that: For sections assigned value A, website traffic is directly proportional to the activity evaluation value (Aev). High website traffic usually indicates more users visiting the website, browsing different pages, and participating in website activities, reflecting the website's popularity and user engagement. Therefore, sections with high website traffic typically have high activity evaluation values. For sections assigned value B, the number of online users is directly proportional to the activity evaluation value (Aev). A high number of online users means more users are online simultaneously, indicating that the section has attracted more user attention and participation, and interacts with more users, thus increasing activity. Therefore, sections with a high number of online users typically have high activity evaluation values. For sections assigned value C, the online time is directly proportional to the activity evaluation value (Aev). A long online time indicates that users stay in the section for a longer period, indicating a higher level of user interest in the section. A longer online time indicates that users are willing to spend more time in the section. Users spend more time browsing, interacting, and participating in a section, reflecting their activity level. Therefore, sections with higher online time typically have higher activity evaluation scores. The interaction data for sections assigned a value of D is directly proportional to the activity evaluation score Aev. High interaction data indicates that users have engaged in more interactive behaviors on the section, such as commenting, sharing, and liking, demonstrating high user participation and positive feedback on the content, further reflecting the section's activity level. Therefore, sections with higher interaction data typically have higher activity evaluation scores. The rebound rate for sections assigned a value of E is inversely proportional to the activity evaluation score. A lower rebound rate indicates higher user interest and participation in the section's content, thus increasing activity. It should be noted that the above relationships represent general trends, and specific situations may vary depending on different business scenarios and user behaviors.

[0084] When calculating the activity assessment value Aev for a corresponding section, since the calculation is performed for the corresponding section, the assigned value A is the same for each section. Therefore, the influence of the assigned value A on the activity assessment value Aev is not considered. Then, a weighted average is calculated for the assigned values ​​B, C, and D. The square of the resulting value is added to the assigned value A, and then subtracted from the natural constant e raised to the power of E. The calculation of the assigned value E here differs from the other assigned values; it is inversely proportional to the activity assessment value Aev. Therefore, it needs to be calculated in conjunction with the natural constant e to ensure the validity of the subsequent values. Finally, the summation and the result are calculated together. The value is multiplied by the constant correction coefficient G to correct the obtained value and ensure the accuracy of the obtained activity assessment value Aev. The size of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the preset ratio coefficient initially set by those skilled in the art for each set of sample data. It can also be said that it is preset according to the actual situation, as long as it does not affect the proportional relationship between the parameter and the quantified value. The same explanation is adopted for the preset ratio coefficient and constant correction coefficient described in other formulas.

[0085] The data statistics module builds a data calculation model and generates the activity stability coefficient Asct of the corresponding sector based on several activity evaluation values ​​Aev obtained within a preset time period T.

[0086] The process of generating the activity stability coefficient Asct for the corresponding sector is as follows:

[0087] First, calculate the average of several activity assessment values ​​(Aev) for the corresponding sectors obtained within a preset time period T, i.e. The activity stability coefficient Asct is then generated based on the following formula:

[0088]

[0089] In the formula, i = {1, 2, 3, ..., s}, s represents the number of activity assessment values ​​(Aev), and s is a positive integer. i This represents different activity assessment values ​​among several activity assessment values ​​Aev; the value of the time period T mentioned above is the same as that of S;

[0090] The activity stability coefficients Asct generated by each section are sorted from largest to smallest, and the sorting results are displayed in the form of a chart to facilitate the identification of the sections with the highest activity levels.

[0091] Specifically, by assigning values ​​to the extracted and labeled dynamic data, and generating the corresponding activity evaluation value Aev based on the assigned dataset, not only can the complexity of data processing be reduced to improve the calculation effect, but the data units can also be automatically unified, avoiding preprocessing of the dataset. To ensure the effectiveness and accuracy of the subsequent evaluation of the activity of each sector, within a preset time period T, an activity stability coefficient Asct is generated based on the activity evaluation value Aev, and then directly observed through charts, demonstrating the effect of the data statistics work.

[0092] Example 2:

[0093] Please see Figure 2 Based on Example 1, this embodiment also provides a dynamic data statistics method using multidimensional data annotation, including the following steps:

[0094] S1. Obtain dynamic data from the website;

[0095] The dynamic data includes: website visits, online users and online time for each section of the website, interaction data for each section of the website, and bounce rate. Website visits represent the total number of times the website is visited in a day. Online users for each section represent the number of users online for each section in a day. Online time for each section represents the total time spent on the corresponding section's page. Interaction data includes the number of comments, likes, and shares. Bounce rate represents the proportion of users who leave the page after visiting the corresponding section without continuing to interact with it.

[0096] S2. Establish a tagging engine and label the acquired dynamic data based on the tagging engine;

[0097] The specific process of annotating the acquired dynamic data is as follows: acquire the website visits, online users and online time of each section of the website, interaction data and rebound rate of each section of the website within a day, compare different dynamic data with the corresponding standard thresholds, and annotate dynamic data that does not exceed the standard threshold and dynamic data that exceeds the standard threshold separately as high dynamic data or low dynamic data.

[0098] S3. Both dynamic data and labeled dynamic data are stored in the database, and an index is built based on a distributed search engine; the specific steps in the data storage module are as follows:

[0099] Data storage: Store dynamic data and annotated dynamic data in the selected database;

[0100] Data synchronization: Synchronizing data stored in the database to the distributed search engine;

[0101] Creating an index: Creating an index for the dataset to be queried. In a distributed search engine, the index creation process involves sharding the data and storing it on different nodes.

[0102] Data retrieval: Using the query syntax and API of the search engine, relevant data is retrieved from the distributed search engine by searching and filtering based on indexed fields and query conditions;

[0103] S4. Build a rule engine to assign values ​​to the extracted and labeled dynamic data. If the data is determined to be high dynamic data, assign it a value of n; if it is determined to be low dynamic data, assign it a value of n-1. Build a data analysis model to generate the corresponding activity evaluation value Aev based on the assigned datasets (including assignment A, assignment B, assignment C, assignment D, and assignment E) for different sections of the website.

[0104] S5. Build a data calculation model. Based on the activity evaluation values ​​Aev of the corresponding sectors obtained within the preset time period T, generate the activity stability coefficient Asct of the corresponding sectors. Sort the activity stability coefficients Asct generated by each sector from largest to smallest and display the sorting results in the form of a chart.

[0105] In the application, the various formulas mentioned are all calculated by removing dimensions and taking their numerical values. The formulas are derived from the most recent real-world situation by collecting a large amount of data and simulating it with software. The formulas are set by those skilled in the art according to the actual situation.

[0106] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0108] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A dynamic data statistics system employing multidimensional data annotation, characterized in that, The system includes: The data acquisition module obtains dynamic data from the website; The data annotation module establishes a tag engine and uses this engine to annotate the acquired dynamic data. The data storage module stores both dynamic data and labeled dynamic data in the database and builds an index based on a distributed search engine. The data analysis model builds a rule engine to assign values ​​to the extracted and labeled dynamic data. When the data is determined to be highly dynamic, it is assigned a value of n, and when it is determined to be low dynamic data, it is assigned a value of n-1. The data analysis model is constructed to generate the corresponding activity evaluation value Aev based on the assigned datasets under the conditions of different sections within the website. The data statistics module builds a data calculation model, generates an activity stability coefficient Asct for the corresponding sector based on several activity evaluation values ​​Aev obtained within a preset time period T, sorts the activity stability coefficients Asct generated for each sector from largest to smallest, and displays the sorting results in the form of a chart.

2. The dynamic data statistics system using multidimensional data annotation according to claim 1, characterized in that: Dynamic data includes: website visits, online users and online time for each section of the website, interaction data for each section of the website, and bounce rate. Website visits represent the total number of times the website is visited in a day. Online users for each section represent the number of users online for each section in a day. Online time for each section represents the total time spent on the corresponding section's page. Interaction data includes the number and sum of comments, likes, and shares. Bounce rate represents the proportion of users who leave the page after visiting the corresponding section without further interaction.

3. A dynamic data statistics system employing multidimensional data annotation as described in claim 2, characterized in that: The specific process of labeling the acquired dynamic data is as follows: acquire the website visits, online users and online time of each section of the website, interaction data and rebound rate of each section of the website within a day, compare different dynamic data with the corresponding standard thresholds, and label dynamic data that does not exceed the standard threshold and dynamic data that exceeds the standard threshold as high dynamic data or low dynamic data respectively.

4. A dynamic data statistics system employing multidimensional data annotation according to claim 3, characterized in that: The specific steps performed in the data storage module are as follows: Data storage: Store dynamic data and annotated dynamic data in the selected database; Data synchronization: Synchronizing data stored in the database to the distributed search engine; Creating an index: Creating an index for the dataset to be queried. In a distributed search engine, the index creation process involves sharding the data and storing it on different nodes. Data retrieval: Using the search engine's query syntax and API, relevant data is retrieved from the distributed search engine by searching and filtering based on indexed fields and query conditions.

5. A dynamic data statistics system employing multidimensional data annotation according to claim 4, characterized in that: The assignment dataset includes assignments A, B, C, D, and E.

6. A dynamic data statistics system employing multidimensional data annotation according to claim 5, characterized in that: The formula used to generate the activity assessment value (Aev) for the corresponding sector is as follows: In the formula, a1, a2, a3, a4, and a5 are the preset proportional coefficients for assignment A, assignment B, assignment C, assignment D, and assignment E, respectively, and a1 > a5 > a4 > a2 > a3 > 0, a1 + a2 + a3 + a4 + a5 = 4.75, G is the constant correction coefficient, and e is the natural constant.

7. A dynamic data statistics system employing multidimensional data annotation according to claim 6, characterized in that: The process of generating the activity stability coefficient Asct for the corresponding sector is as follows: First, calculate the average of several activity assessment values ​​(Aev) for the corresponding sectors obtained within a preset time period T, i.e. Then, the activity stability coefficient Asct is generated based on the following formula: In the formula, i = {1, 2, 3, ..., s}, s represents the number of activity assessment values ​​(Aev), and s is a positive integer. i This represents different activity evaluation values ​​among several activity evaluation values ​​(Aev).

8. A dynamic data statistics method employing multidimensional data annotation, using the system described in any one of claims 1 to 7, characterized in that, Includes the following steps: S1. Obtain dynamic data from the website; The dynamic data includes: website visits, online users and online time for each section of the website, interaction data for each section of the website, and bounce rate. Website visits represent the total number of times the website is visited in a day. Online users for each section represent the number of users online for each section in a day. Online time for each section represents the total time spent on the corresponding section's page. Interaction data includes the number of comments, likes, and shares. Bounce rate represents the proportion of users who leave the page after visiting the corresponding section without continuing to interact with it. S2. Establish a tagging engine and label the acquired dynamic data based on the tagging engine; The specific process of annotating the acquired dynamic data is as follows: acquire the website visits, online users and online time of each section of the website, interaction data and rebound rate of each section of the website within a day, compare different dynamic data with the corresponding standard thresholds, and annotate dynamic data that does not exceed the standard threshold and dynamic data that exceeds the standard threshold separately as high dynamic data or low dynamic data. S3. Both dynamic data and labeled dynamic data are stored in the database, and an index is built based on a distributed search engine; the specific steps in the data storage module are as follows: Data storage: Store dynamic data and annotated dynamic data in the selected database; Data synchronization: Synchronizing data stored in the database to the distributed search engine; Creating an index: Creating an index for the dataset to be queried. In a distributed search engine, the index creation process involves sharding the data and storing it on different nodes. Data retrieval: Using the query syntax and API of the search engine, relevant data is retrieved from the distributed search engine by searching and filtering based on indexed fields and query conditions; S4. Build a rule engine to assign values ​​to the extracted and labeled dynamic data. If the data is determined to be high dynamic data, assign it a value of n; if it is determined to be low dynamic data, assign it a value of n-1. Build a data analysis model to generate the corresponding activity evaluation value Aev based on the assigned datasets (including assignment A, assignment B, assignment C, assignment D, and assignment E) for different sections of the website. S5. Build a data calculation model. Based on the activity evaluation values ​​Aev of the corresponding sectors obtained within the preset time period T, generate the activity stability coefficient Asct of the corresponding sectors. Sort the activity stability coefficients Asct generated by each sector from largest to smallest and display the sorting results in the form of a chart.

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