Social media platform interaction data display method and device based on Tab switching

Abstract

The embodiment of the invention provides a social media platform interaction data display method and device based on Tab switching and electronic equipment, and belongs to the technical field of data processing.The method comprises the steps that user interaction data are obtained through an API construction tool, and standard data are obtained through preprocessing and stored; obtaining a feature vector set according to m feature value calculation functions, sorting the feature vector set according to time, and performing time sequence feature calculation to obtain a new feature vector; the standard data are clustered, and part of feature values are screened out through matching degree calculation; and constructing p different style display components for the screened characteristic values, and displaying the standard data in p paging pages through Tab switching to realize multi-dimensional display and interaction of the data. According to the method, an efficient data processing and transmission mechanism is adopted, real-time updating and displaying of the data are achieved, timely, accurate and comprehensive data analysis results are provided for users, and the social media platform data visualization effect and the user experience are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device and electronic device for displaying interactive data on a social media platform based on tab switching. Background Art

[0002] In today's era of booming social media, social media platforms have accumulated a massive amount of user interaction data. This data contains huge commercial value and user insight information. For enterprises and platform operators, how to effectively display and analyze this data in order to make scientific decisions has become a key issue.

[0003] Existing methods for displaying interactive data on social media platforms have many shortcomings:

[0004] First, data processing methods are not perfect. The data obtained from different social media platforms are in various formats and of varying quality. Existing data preprocessing methods are often unable to efficiently clean, convert, and integrate these data, which affects the accuracy and reliability of subsequent analysis.

[0005] Secondly, the dimensions of data analysis are limited. Traditional methods can usually only calculate some simple statistical indicators, and it is difficult to deeply explore the complex characteristics and potential patterns behind the data. The inability to comprehensively and deeply analyze data makes it difficult for users to understand the operating status of social media platforms and user behavior characteristics from multiple perspectives.

[0006] Furthermore, data display lacks flexibility and interactivity. Most existing display methods use fixed charts or reports. Users can only passively accept the established display content and cannot flexibly switch the display format or filter the data of interest according to their own needs. This single display method is difficult to meet the diverse needs of different users in different scenarios, reducing the user's understanding and utilization efficiency of data.

[0007] In addition, lack of real-time performance is also a prominent problem. Social media data is characterized by dynamic changes, and existing display methods often cannot reflect the latest changes in data in a timely manner, resulting in users obtaining delayed information and being unable to make corresponding decisions in a timely manner.

[0008] The above problems become technical problems that need to be solved. Summary of the invention

[0009] In view of this, the embodiments of the present invention provide a method, device and electronic device for displaying interactive data on a social media platform based on Tab switching, which at least partially solve the problems existing in the prior art.

[0010] In a first aspect, an embodiment of the present invention provides a method for displaying interactive data on a social media platform based on tab switching, comprising:

[0011] Using an API construction tool to regularly obtain social media user interaction data DJ, preprocessing the interaction data DJ to obtain standard data DB, and storing the standard data DB in a database;

[0012] According to the pre-constructed m eigenvalue calculation functions, eigenvalue calculation is performed on the standard data stored in the database to obtain a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB;

[0013] Obtain the standard time interval Tq, arrange the feature vectors in the feature vector set M in time order within the time interval to obtain a time series vector set M'={M'1,M'2,...M'm}, perform time series feature calculation on the time series vector set M' to obtain a feature vector K={K1,K2,...Km} containing m feature values;

[0014] Perform clustering calculation on the standard data DB to obtain a clustering vector J={J1,J2,…Jn} containing n clustering points, calculate the matching value Pz=Fp(J,K) between the clustering vector J and the eigenvector K through the matching calculation function Fp, filter out the eigenvalues ​​Ki whose matching values ​​are greater than the preset value P0, and form a screening vector K'={K'1,K'2,…K'p} containing p eigenvalues, p≤m;

[0015] P data display components of different styles are constructed for the screening vector K', and the p data display components of different styles display the standard data DB in p pagination pages by means of Tab switching.

[0016] According to a specific implementation of the embodiment of the present invention, the data preprocessing of the interactive data DJ to obtain standard data DB includes:

[0017] Calculate the mean of attribute A in the interaction data as , is the non-missing value of the attribute A, is the number of non-missing values, Fill missing values ​​in interaction data;

[0018] Assume Q1 and Q3 are the lower quartile and upper quartile of the interaction data, respectively, set IQR = Q3-Q1, and regard data less than Q1-1.5×IQR or greater than Q3+1.5×IQR as outliers and correct or delete them.

[0019] According to a specific implementation of the embodiment of the present invention, the data preprocessing of the interactive data DJ to obtain the standard data DB also includes:

[0020] For the numerical data in the interactive data, let the mean of the data x be μ and the standard deviation be σ, then the standardized data x'= ;

[0021] Encode categorical data in interaction data, mapping each category into a unique binary vector.

[0022] According to a specific implementation of an embodiment of the present invention, the eigenvalue calculation is performed on the standard data stored in the database according to the pre-constructed m eigenvalue calculation functions to obtain a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB, including:

[0023] For each eigenvalue calculation function fi (i=1,2,...,m), operate on the relevant data fields in the standard data DB. Suppose the data set involved in the calculation in the standard data DB is D, and each eigenvector Mi is calculated by Mi=fi(D), where Mi contains the calculation result of the corresponding eigenvalue, thereby forming a eigenvector set M={M1,M2,...Mm}.

[0024] According to a specific implementation of an embodiment of the present invention, the method of performing eigenvalue calculation on the standard data stored in the database according to the pre-constructed m eigenvalue calculation functions to obtain a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB also includes:

[0025] When calculating each eigenvector Mi, record the intermediate parameters and statistical information during the calculation process;

[0026] Perform a preliminary quality assessment on the calculated feature vector set M to check whether there are abnormal or unreasonable feature values. If so, adjust or recalculate them.

[0027] According to a specific implementation of the embodiment of the present invention, the feature vectors in the feature vector set M are arranged in time order within the time interval to obtain a time series vector set M'={M'1, M'2, ...M'm}, and the time series feature calculation is performed on the time series vector set M' to obtain a feature vector K={K1, K2, ...Km} containing m feature values, including:

[0028] Determine the start time t1 and end time t2 of the time interval, extract the feature vectors in the corresponding time interval from the feature vector set M, and arrange them in chronological order to form a time series vector set M';

[0029] set up is the value of the time series at time t, is the autoregression coefficient, is the moving average coefficient, is a white noise sequence, p is the autoregressive order, q is the moving average order, then Calculate M' and obtain the eigenvector K={K1,K2,...Km} containing m eigenvalues.

[0030] According to a specific implementation of an embodiment of the present invention, clustering calculation is performed on the standard data DB to obtain a clustering vector J={J1, J2, ... Jn} containing n clustering points, and a matching value Pz=Fp(J, K) between the clustering vector J and the feature vector K is calculated through a matching calculation function Fp, and the feature value Ki with a matching value greater than a preset value P0 is screened out to form a screening vector K'={K'1, K'2, ... K'p} containing p feature values, including:

[0031] Assume that the initial values ​​of cluster centers are C1, C2, ..., Cn, and perform iterative calculations on the data points in the standard data DB so that each data point is assigned to the nearest cluster center. After multiple iterations, stable n cluster points are obtained to form a cluster vector J = {J1, J2, ...Jn};

[0032] The matching value Pz between J and K is calculated through the matching calculation function Fp, which is determined according to specific business requirements and data characteristics;

[0033] Compare Pz with the preset value P0, filter out the eigenvalue Ki where Pz>P0, and form a screening vector K'={K'1,K'2,...K'p}.

[0034] According to a specific implementation of an embodiment of the present invention, constructing p data display components of different styles for the screening vector K', wherein the p data display components of different styles display the standard data DB in p pagination pages by means of Tab switching, includes:

[0035] For each eigenvalue K'i (i=1,2,...,p), design a corresponding data display style according to its data characteristics and business needs;

[0036] Using the front-end framework, create p data display components, each corresponding to a characteristic value K'i, and place these components on p pagination pages respectively;

[0037] By setting the Tab switching function, users can switch between different paging pages to view the display of standard data DB related to different feature values.

[0038] In a second aspect, an embodiment of the present invention further provides a device for displaying interactive data of a social media platform based on Tab switching, comprising:

[0039] An acquisition module, using an API construction tool to regularly acquire social media user interaction data DJ, preprocessing the interaction data DJ to obtain standard data DB, and storing the standard data DB in a database;

[0040] A calculation module performs eigenvalue calculation on the standard data stored in the database according to the pre-built m eigenvalue calculation functions, and obtains a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB;

[0041] an arrangement module, obtaining the standard time interval Tq, arranging the feature vectors in the feature vector set M in the time interval in chronological order to obtain a time series vector set M'={M'1,M'2,...M'm}, performing time series feature calculation on the time series vector set M' to obtain a feature vector K={K1,K2,...Km} containing m feature values;

[0042] The screening module performs clustering calculation on the standard data DB to obtain a clustering vector J={J1,J2,…Jn} containing n clustering points, calculates the matching value Pz=Fp(J,K) between the clustering vector J and the eigenvector K through the matching calculation function Fp, and screens out the eigenvalues ​​Ki whose matching values ​​are greater than the preset value P0 to form a screening vector K'={K'1,K'2,…K'p} containing p eigenvalues, where p≤m;

[0043] The display module constructs p data display components of different styles for the screening vector K', and the p data display components of different styles display the standard data DB in p pagination pages by means of Tab switching.

[0044] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0045] at least one processor; and,

[0046] a memory communicatively connected to the at least one processor; wherein,

[0047] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in any of the first aspects or any of the implementations of the first aspect.

[0048] In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the method described in the first aspect or any implementation of the first aspect.

[0049] In the fifth aspect, an embodiment of the present invention further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the method described in the first aspect or any implementation of the first aspect.

[0050] The scheme in the embodiment of the present invention includes: using an API construction tool to regularly obtain social media user interaction data DJ, performing data preprocessing on the interaction data DJ to obtain standard data DB, and storing the standard data DB in the database; performing eigenvalue calculation on the standard data stored in the database according to m eigenvalue calculation functions constructed in advance to obtain m eigenvector sets M={M1,M2,…Mm} related to the standard data DB; obtaining the standard time interval Tq, arranging the eigenvectors in the eigenvector set M in chronological order within the time interval to obtain a time series vector set M'={M'1,M'2,…M'm}, and performing time series vector calculation on the time series vector set M'. The method further comprises the following steps: calculating the sequence characteristics of the clustered data, obtaining a feature vector K={K1, K2, ...Km} containing m feature values; performing clustering calculation on the standard data DB, obtaining a clustering vector J={J1, J2, ...Jn} containing n clustering points, calculating the matching value Pz=Fp(J, K) between the clustering vector J and the feature vector K through the matching calculation function Fp, filtering out the feature values ​​Ki with matching values ​​greater than a preset value P0, and forming a screening vector K'={K'1, K'2, ...K'p} containing p feature values, where p ≤ m; constructing p data display components of different styles for the screening vector K', and displaying the standard data DB on p paginated pages by switching between tabs.

[0051] This solution has the following beneficial effects:

[0052] Using API to build tools to regularly obtain social media user interaction data can cover multiple data sources and multiple types of interaction data to ensure the comprehensiveness and timeliness of the data. By managing and coordinating different data sources and unifying data formats, a high-quality data foundation is provided for subsequent analysis and processing, avoiding problems caused by inconsistent data and confusing formats, and greatly improving data processing efficiency.

[0053] In the data preprocessing stage, a series of scientific methods, such as removing duplicate data, handling missing values ​​and outliers, standardization and coding, etc., have been used to improve the quality and standardization of data. This makes subsequent operations such as eigenvalue calculation and cluster analysis more accurate and reliable, laying a solid foundation for in-depth mining of data value.

[0054] We selected a database system suitable for big data processing to store standard data and created indexes for high-frequency query fields, which significantly improved the speed and performance of data queries. At the same time, we adopted a strategy that combines incremental backup with full backup to ensure data security and reliability, prevent data loss, and provide data-level protection for enterprises and platform operators.

[0055] By calculating the standard data based on multiple pre-built eigenvalue calculation functions, it is possible to extract data features from different angles and obtain a rich set of eigenvectors. These eigenvalues ​​cover multiple aspects of social media platform operations and user behavior, providing a comprehensive perspective for in-depth understanding of platform conditions and user needs, and helping to discover potential business opportunities and problems.

[0056] Arranging the feature vectors in chronological order to form a time series vector set and performing time series feature calculations can capture the changing trends and patterns of data over time. This analysis method can predict future development trends and help companies and platforms formulate corresponding strategies in advance to adapt to market changes.

[0057] The standard data is clustered to obtain a cluster vector, and the feature values ​​with a high degree of match with the cluster vector are screened out through the matching calculation function to form a screening vector. This method can deeply explore the potential patterns and associations in the data, classify and integrate data with similar characteristics, and provide strong support for targeted data analysis and decision-making.

[0058] Build multiple data display components of different styles for the filter vectors, and display standard data on the paginated page by switching between tabs. Users can easily switch between different display formats according to their needs and preferences, and view data from different angles, meeting the diverse needs of different users in different scenarios and greatly improving users' understanding and utilization efficiency of data.

[0059] The interactive mode of tab switching is simple and intuitive, and users can quickly get started. This interactive design enhances the interactivity between users and data, so that users are no longer passively accepting data display, but can actively participate in data exploration, and choose appropriate display content according to their own focus and analysis purpose, which improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A schematic flow chart of a method for displaying interactive data on a social media platform based on Tab switching provided by an embodiment of the present invention;

[0062] Figure 2 A schematic flow chart of another method for displaying interactive data on a social media platform based on Tab switching provided by an embodiment of the present invention;

[0063] Figure 3 A schematic diagram of the structure of a social media platform interactive data display device based on Tab switching provided by an embodiment of the present invention;

[0064] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0066] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0067] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0068] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0069] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0070] The disclosed embodiment provides a method for displaying interactive data on a social media platform based on tab switching. The method for displaying interactive data on a social media platform based on tab switching provided in this embodiment can be executed by a computing device, which can be implemented as software, or as a combination of software and hardware, and which can be integrated in a server, a terminal device, etc.

[0071] See also Figure 1 and Figure 2 The present disclosure provides a method for displaying interactive data on a social media platform based on tab switching, including:

[0072] S101, using an API building tool to regularly obtain social media user interaction data DJ, performing data preprocessing on the interaction data DJ to obtain standard data DB, and storing the standard data DB in a database.

[0073] Use programming languages ​​(such as Python) combined with corresponding network request libraries (such as requests) to build automation tools. By writing scripts and setting scheduled tasks (such as using Python's schedule library), automatically call the API interface at predetermined time intervals (such as every day, every hour, etc.) to obtain user interaction data. In the process of obtaining data, handle possible abnormal situations, such as network request failure, API response timeout, etc., and ensure the integrity of data acquisition by setting up a retry mechanism. Preprocess the interactive data DJ to obtain standard data DB:

[0074] The obtained interaction data DJ is stored in a suitable data structure (such as DataFrame), and the functions of the data processing library (such as Pandas) are used to identify and delete duplicate records by comparing the unique identification fields of the data (such as user ID, interaction record ID, etc.).

[0075] For numeric fields, calculate the mean of the non-missing values ​​of the field and use the mean to fill the missing values; for categorical fields, use the mode to fill the missing values. For numeric data, calculate the lower quartile Q1 and the upper quartile Q3, and determine the interquartile range IQR = Q3-Q1. Data that is less than Q1-1.5×IQR or greater than Q3+1.5×IQR is considered an outlier and can be modified (such as replaced with a nearby reasonable value) or deleted according to the specific situation.

[0076] Convert data fields from different sources to a unified data type, such as converting all date fields to a standard date and time format, and converting string numbers to numeric types. For categorical data, such as user gender, region, etc., use One-Hot Encoding or Label Encoding to convert them to numeric form to facilitate subsequent calculations and analysis.

[0077] Classify and organize the data according to its different attributes and uses. For example, store different types of interactive data such as the number of likes, comments, and shares in different columns or tables. Select a suitable database system based on the amount of data and business needs, such as a relational database (such as MySQL, PostgreSQL) or a non-relational database (such as MongoDB). Use the corresponding database connection library (such as mysql-connector-python for MySQL and pymongo for MongoDB) to establish a connection with the database. Insert or import the preprocessed standard data DB into the corresponding table according to the database table structure requirements.

[0078] S102, performing eigenvalue calculation on the standard data stored in the database according to the pre-constructed m eigenvalue calculation functions, and obtaining m eigenvector sets M={M1, M2, ...Mm} related to the standard data DB.

[0079] Based on the pre-built m eigenvalue calculation functions, the eigenvalue calculation is performed on the standard data stored in the database:

[0080] Read standard data from the database DB and load it into a suitable data processing environment (such as Python's DataFrame).

[0081] For each pre-built eigenvalue calculation function fi (i=1,2,...,m), operate the data according to the function definition. For example, to calculate the user's activity eigenvalue, you can define function f1 as the weighted sum of indicators such as the number of logins, number of content releases, and number of interactions a user has participated in within a certain period of time. By traversing each record in the data set, applying the corresponding function calculation to obtain the eigenvalue of each sample, thus forming the eigenvector Mi.

[0082] All eigenvectors Mi are combined together to form a eigenvector set M={M1,M2,...Mm}.

[0083] S103, obtaining the standard time interval Tq, arranging the feature vectors in the feature vector set M in chronological order within the time interval to obtain a time series vector set M'={M'1,M'2,…M'm}, performing time series feature calculation on the time series vector set M' to obtain a feature vector K={K1,K2,…Km} containing m eigenvalues.

[0084] Get the standard time interval Tq: Determine the start and end time of the time interval based on business needs and analysis purposes. For example, if you want to analyze the user's behavior trends in the past week, the time interval Tq is the time range of the past week.

[0085] Arrange the feature vectors in the feature vector set M in chronological order within the time interval to obtain the time series vector set M':

[0086] Ensure that each feature vector in the feature vector set M contains time-related information, such as a timestamp field. Sort the data in each feature vector in chronological order according to the timestamp field. Combine the sorted feature vectors into a time series vector set M'={M'1,M'2,...M'm}.

[0087] Use time series analysis algorithms (such as ARIMA model, moving average model, etc.) to process the time series vector set M'. Taking the ARIMA model as an example, first perform a stationarity test on the time series data (such as using the Augmented Dickey-Fuller test). If the data is not stationary, perform differential processing to make it stable. Determine the parameters of the model (such as autoregressive order p, differential order d, moving average order q), and calculate the predicted value or eigenvalue by fitting the model. Perform the above operation on each time series vector M'i to obtain a eigenvector K={K1,K2,...Km} containing m eigenvalues.

[0088] S104, perform clustering calculation on the standard data DB to obtain a clustering vector J={J1,J2,…Jn} containing n clustering points, calculate the matching value Pz=Fp(J,K) between the clustering vector J and the eigenvector K through the matching calculation function Fp, filter out the eigenvalues ​​Ki whose matching values ​​are greater than the preset value P0, and form a screening vector K'={K'1,K'2,…K'p} containing p eigenvalues, p≤m.

[0089] Select a suitable clustering algorithm, such as K-Means algorithm. Determine the number of clusters n. You can use methods such as Elbow Method or Silhouette Coefficient to select the optimal number of clusters. Take the data in the standard data DB as input, apply the selected clustering algorithm, and after multiple iterative calculations, assign each data point to the nearest cluster center, and finally obtain stable n cluster points to form a cluster vector J={J1,J2,...Jn}.

[0090] Define the matching calculation function Fp according to business needs and data characteristics. For example, you can use the cosine similarity function to calculate the similarity between two vectors. The cosine similarity formula is Fp(J,K)=(J⋅K) / |J||K|, where J·K is the dot product of vectors J and K, and |J| and |K| are the moduli of vectors J and K, respectively.

[0091] The eigenvalues ​​Ki whose matching values ​​are greater than the preset value P0 are screened out to form a screening vector K' containing p eigenvalues. A preset value P0 is set, which can be determined based on experience or multiple experiments. Each eigenvalue Ki in the eigenvector K is traversed and compared with the matching value Pz. If Pz is greater than P0, the eigenvalue Ki is screened out, and the screened eigenvalues ​​are combined into a screening vector K'={K'1, K'2, ... K'p} containing p eigenvalues, where p≤m.

[0092] S105 , constructing p data display components of different styles for the screening vector K′, and the p data display components of different styles display the standard data DB in p pagination pages by means of Tab switching.

[0093] Use front-end development technologies (such as HTML, CSS, JavaScript) combined with data visualization libraries (such as Echarts, D3.js, etc.) to build data display components. Choose the appropriate visualization method according to the characteristics of each eigenvalue and business needs. For example, for numerical eigenvalues, you can use a bar chart to show the differences between different samples, and a line chart to show its trend over time; for categorical eigenvalues, you can use a pie chart to show the proportion of each category. Create an independent visualization component for each eigenvalue K'i, set the component style, interaction effects, etc., so that it can clearly and intuitively display data information.

[0094] Use front-end frameworks (such as React, Vue.js, etc.) to manage page structure and status. Create p paginated pages and place a data display component on each page. By setting the Tab switching function, using HTML tags and JavaScript event monitoring mechanism, when users click on different Tab tags, the corresponding paginated pages and data display components are displayed, realizing convenient switching display of different feature value data, so that users can view the standard data DB from different angles.

[0095] According to a specific implementation of the embodiment of the present invention, the step of performing data preprocessing on the interactive data DJ to obtain standard data DB includes:

[0096] Calculate the mean of attribute A in the interaction data as , is the non-missing value of the attribute A, is the number of non-missing values, Fill missing values ​​in interaction data;

[0097] Assume Q1 and Q3 are the lower quartile and upper quartile of the interaction data, respectively, set IQR = Q3-Q1, and regard data less than Q1-1.5×IQR or greater than Q3+1.5×IQR as outliers and correct or delete them.

[0098] As an optional implementation, a React-based dynamic highlight bar chart may be used to display the standard data DB. Specifically, the following steps S1051-S1055 may be included:

[0099] S1051, obtain the dynamic highlight bar chart set P to be displayed from the standard data DB, extract the configuration parameters of the dynamic highlight bar chart set P, obtain the configuration parameter set C={C1,C2,...,Cn}, set the weight parameter set W={W1,W2,...,Wn} corresponding to the configuration parameter set C, and execute the bar chart generation function Fc of the configuration parameter set C and the weight parameter set W.

[0100] During the operation of the system, in order to meet the user's demand for dynamic highlight bar chart display, firstly, a set P of dynamic highlight bar charts to be displayed is obtained from the standard data DB.

[0101] Extract configuration parameters from the dynamic highlight bar graph set P. Each dynamic highlight bar graph has a series of specific configuration parameters, which determine the appearance of the bar graph, data display method and other characteristics. Through a specific parsing algorithm, extract all relevant configuration parameters from the set P to obtain the configuration parameter set C = {C1, C2,..., Cn}.

[0102] In order to make reasonable calculations based on the importance of different configuration parameters when generating histograms, a weight parameter set W = {W1, W2, …, Wn} corresponding to the configuration parameter set C is set. Each weight parameter Wi corresponds to a configuration parameter Ci, reflecting the relative importance of the configuration parameter in the histogram generation process.

[0103] At the same time, a histogram generation function Fc is defined, which takes the configuration parameter set C and the weight parameter set W as input, and generates the required histogram data through complex algorithms and logic. The specific implementation of the histogram generation function Fc may involve multiple steps such as data query, calculation, and formatting to ensure that the generated histogram data is accurate and meets user expectations.

[0104] S1052, receiving the setting parameters Cv input by the user for the bar graph in the operation interface, generating the matching configuration parameters Cp∈C and matching weight parameters Wp∈W of the bar graph based on the setting parameters Cv, inputting the matching configuration parameters Cp and matching weight parameters Wp into the bar graph generation function Fc, and obtaining the bar graph original data Dy=Fc(Cp,Wp).

[0105] Receive the setting parameters Cv input by the user for the bar graph in the operation interface. The user can personalize the bar graph according to his or her needs in the operation interface. These setting parameters Cv include the user's specific requirements for the bar graph, such as the color, data range, sorting method, etc. of the bar graph.

[0106] Based on the setting parameter Cv, search for the matching configuration parameter Cp in the configuration parameter set C. The matching process may involve parsing the setting parameter Cv and comparing it with each element in the configuration parameter set C, and finding the configuration parameter Cp that best meets the user's needs through a specific matching algorithm, so that Cp ∈ C.

[0107] Similarly, find the matching weight parameter Wp corresponding to Cp in the weight parameter set W, so that Wp ∈ W. The matching weight parameter Wp will be used in subsequent calculations to ensure that the influence of each configuration parameter can be reasonably reflected when generating the histogram.

[0108] Input the matching configuration parameter Cp and the matching weight parameter Wp into the histogram generation function Fc, and obtain the histogram raw data Dy = Fc(Cp, Wp) through the function operation and processing. The histogram raw data Dy contains the basic data information required to generate the histogram.

[0109] S1053, parse the bar chart original data Dy to obtain the data processing task Tk1 and the data rendering task Tk2 required to display the bar chart, calculate the time complexity of the data processing task Tk1 to be O(Tk1) and the time complexity of the data rendering task to be O(Tk2), and based on the time complexity of O(Tk1) and the time complexity of O(Tk2), convert the bar chart original data Dy into split data Dcf that is executed simultaneously in k different threads.

[0110] The original data Dy of the bar chart is parsed and decomposed into two main tasks required to display the bar chart through a specific parsing algorithm: data processing task Tk1 and data rendering task Tk2.

[0111] The data processing task Tk1 is mainly responsible for preprocessing the raw data, such as data cleaning, screening, and calculating statistical indicators to ensure the accuracy and availability of the data. The time complexity of calculating the data processing task Tk1 is O(Tk1), which reflects the increasing trend of the time required to execute the task as the amount of data increases.

[0112] The data rendering task Tk2 is responsible for converting the processed data into a visual bar chart, including determining the appearance attributes of the bar chart, such as position, size, and color, and mapping the data to each element of the bar chart. The time complexity of calculating the data rendering task Tk2 is O(Tk2).

[0113] Specifically, let the data size be s, and the data processing task is used to sort the data, and the time complexity O(Tk01) satisfies the formula:

[0114]

[0115] in It represents the asymptotically tight bound of the algorithm, and its time complexity is of the same order as slogs;

[0116] For the data rendering task Tk02, which involves drawing and styling r graphic elements, the time complexity O(Tk02) is expressed by the following formula:

[0117]

[0118] f(i) is a function related to the rendering of the i-th graphic element, which includes the attribute calculation and position adjustment operations of the element;

[0119] Set the comprehensive complexity evaluation function G(O(Tk01), O(Tk02)) to determine the strategy of splitting the bar chart original data Dy into k0 parts, which is defined as:

[0120]

[0121] Where α and β are weight coefficients;

[0122] According to the determined splitting strategy, the original bar graph data Dy is divided according to the task type and data relevance, and the related data and operations are allocated to different parts to form k0 data subsets;

[0123] Data subset The dependency relationship with other subsets is represented by the dependency matrix R. Representation Subset right The degree of dependence, the dependence matrix R satisfies:

[0124]

[0125] The data subsets that have been divided and added with metadata are combined into split data Dcf. The split data Dcf is stored in a tree row structure. Each node represents a data subset, and the edges between nodes represent data dependencies. Suppose the depth of the tree is h and the number of nodes is k0. The depth h of the tree and the number of nodes k0 satisfy the relationship:

[0126]

[0127] in Indicates a floor operation.

[0128] Based on the time complexity of O(Tk1) and the time complexity of O(Tk2), in order to improve the efficiency of data processing and rendering, multi-threading technology is used to convert the original bar chart data Dy into split data DCF executed simultaneously in k different threads. The process of splitting data needs to consider the independence and parallelism of the data. The original data is divided into k parts through a reasonable algorithm, so that each part can be processed in an independent thread, thereby making full use of the advantages of multi-core processors and speeding up data processing and rendering.

[0129] S1054, generating a corresponding execution plug-in for each thread running the split data DCF, forming an execution plug-in set P={P1, P2, . . . , Pk}, and generating the display data DP of the bar chart based on the execution plug-in set.

[0130] A corresponding execution plug-in is generated for each thread running the split data Dcf, forming an execution plug-in set P = {P1, P2,..., Pk}. Each execution plug-in Pi is responsible for managing and executing the data processing and rendering tasks of the corresponding thread. It contains various resources and logics required by the thread, such as data processing functions, rendering algorithms, etc.

[0131] For each data subset in the split data Dcf, analyze its corresponding task requirements and functional requirements, determine the specific operations and functions that each thread needs to perform, and for the i-th data subset, its task complexity is expressed by the complexity function To measure:

[0132]

[0133] in is the number of operations associated with this subset of data, is the complexity function of the jth operation, is the relevant parameter of the jth operation;

[0134] According to the tasks and functional requirements of each thread, design the corresponding execution plug-in architecture, determine the plug-in input interface, output interface and internal processing logic framework, and the data format received by the input interface is vector The data format of the output interface is vector , and there is a linear transformation relationship between the input and output, through a matrix express:

[0135]

[0136] in is an r×s matrix, where r and s are the dimensions of the output vector and the input vector respectively. is an offset vector;

[0137] According to the designed execution plug-in architecture, write specific code to implement each execution plug-in and conduct functional tests on each written execution plug-in;

[0138] Add necessary configuration parameters and initialization logic to each execution plug-in to enable it to be flexibly configured according to different operating environments and requirements. The configuration parameter set is , which contains p parameters , and the relationship between the performance of the plug-in and the configuration parameters is described by the response surface function as follows:

[0139]

[0140] where and e are model parameters.

[0141] Each plug-in in the execution plug-in set runs independently and processes the split data Dcf in its own thread. Through the collaborative work of the execution plug-ins, the processed data is integrated and summarized, and the display data DP of the bar chart is generated based on the execution plug-in set. The display data DP is the bar chart data that can be directly displayed on the interface after being processed and rendered.

[0142] S1055, obtain the data volume Nd displayed by the bar chart on each page. Based on the data volume Nd, divide the display data DP to obtain a sequence of paged data sets D’={D’1, D’2, …, D’m}, where , represents rounding up the numerical value, |DP| represents the number of elements contained in the display data DP. When the user switches the page of the current page with page number p, if the user clicks the previous page and p>1, then p = p - 1; if the user clicks the next page and p<m, then p = p + 1. When the user switches to the q-th page, load the corresponding data D’p from the sequence of paged data sets D’ according to the page number and display the dynamic highlighted bar chart corresponding to the data of this page on the page of the q-th page.

[0143] Obtain the data volume Nd displayed by the bar chart on each page. This data volume is preset according to factors such as interface layout and user experience to ensure that the display effect of the bar chart on each page is clear, beautiful and easy to operate.

[0144] The display data DP is segmented based on the data volume Nd, and the total number of pages m required is calculated according to the formula m = ⌈|DP| / Nd⌉, where ⌈⌉ represents the rounding up of the logarithmic value, and |DP| represents the number of elements contained in the display data DP. Through the segmentation operation, the display data DP is divided into multiple parts to form a paging data set sequence D' = {D'1, D'2, …, D'm}, and each set D'i corresponds to a page of display data.

[0145] When the user switches to the current page with page number p, the system will process the page accordingly. If the user clicks the previous page, first determine whether the current page number p is greater than 1. If p > 1, update the page number p to p - 1. If the user clicks the next page, determine whether the current page number p is less than the total number of pages m. If p < m, update the page number p to p + 1.

[0146] When the user switches to the qth page, the system loads the corresponding data D'p from the paging data set sequence D' according to the page number. Then, the loaded data D'p is rendered and displayed, and the dynamic highlight bar chart corresponding to the page data is presented on the qth page, thereby enabling the user to browse and interact with the dynamic highlight bar chart by page.

[0147] According to a specific implementation of the embodiment of the present invention, the data preprocessing of the interactive data DJ to obtain the standard data DB also includes:

[0148] For the numerical data in the interactive data, let the mean of the data x be μ and the standard deviation be σ, then the standardized data x'= ;

[0149] Encode categorical data in interaction data, mapping each category into a unique binary vector.

[0150] According to a specific implementation of an embodiment of the present invention, the eigenvalue calculation is performed on the standard data stored in the database according to the pre-constructed m eigenvalue calculation functions to obtain a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB, including:

[0151] For each eigenvalue calculation function fi (i=1,2,...,m), operate on the relevant data fields in the standard data DB. Suppose the data set involved in the calculation in the standard data DB is D, and each eigenvector Mi is calculated by Mi=fi(D), where Mi contains the calculation result of the corresponding eigenvalue, thereby forming a eigenvector set M={M1,M2,...Mm}.

[0152] According to a specific implementation of an embodiment of the present invention, the method of performing eigenvalue calculation on the standard data stored in the database according to the pre-constructed m eigenvalue calculation functions to obtain a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB also includes:

[0153] When calculating each eigenvector Mi, record the intermediate parameters and statistical information during the calculation process;

[0154] Perform a preliminary quality assessment on the calculated feature vector set M to check whether there are abnormal or unreasonable feature values. If so, adjust or recalculate them.

[0155] According to a specific implementation of the embodiment of the present invention, the feature vectors in the feature vector set M are arranged in time order within the time interval to obtain a time series vector set M'={M'1, M'2, ...M'm}, and the time series feature calculation is performed on the time series vector set M' to obtain a feature vector K={K1, K2, ...Km} containing m feature values, including:

[0156] Determine the start time t1 and end time t2 of the time interval, extract the feature vectors in the corresponding time interval from the feature vector set M, and arrange them in chronological order to form a time series vector set M';

[0157] set up is the value of the time series at time t, is the autoregression coefficient, is the moving average coefficient, is a white noise sequence, p is the autoregressive order, q is the moving average order, then Calculate M' and obtain the eigenvector K={K1,K2,...Km} containing m eigenvalues.

[0158] According to a specific implementation of an embodiment of the present invention, clustering calculation is performed on the standard data DB to obtain a clustering vector J={J1, J2, ... Jn} containing n clustering points, and a matching value Pz=Fp(J, K) between the clustering vector J and the feature vector K is calculated through a matching calculation function Fp, and the feature value Ki with a matching value greater than a preset value P0 is screened out to form a screening vector K'={K'1, K'2, ... K'p} containing p feature values, including:

[0159] Assume that the initial values ​​of cluster centers are C1, C2, ..., Cn, and perform iterative calculations on the data points in the standard data DB so that each data point is assigned to the nearest cluster center. After multiple iterations, stable n cluster points are obtained to form a cluster vector J = {J1, J2, ...Jn};

[0160] The matching value Pz between J and K is calculated through the matching calculation function Fp, which is determined according to specific business requirements and data characteristics;

[0161] Compare Pz with the preset value P0, filter out the eigenvalue Ki where Pz>P0, and form a screening vector K'={K'1,K'2,...K'p}.

[0162] See also Figure 2 According to a specific implementation of an embodiment of the present invention, constructing p data display components of different styles for the screening vector K', and the p data display components of different styles display the standard data DB in p paging pages by Tab switching, including:

[0163] S201, for each eigenvalue K'i (i=1,2,...,p), design a corresponding data display style according to its data characteristics and business requirements;

[0164] S202, using the front-end framework, create p data display components, each component corresponds to a characteristic value K'i, and place these components on p pagination pages respectively;

[0165] S203, by setting the Tab switching function, the user can switch between different paging pages to view the display of the standard data DB related to different feature values.

[0166] Corresponding to the above method embodiment, see Figure 3 The embodiment of the present invention further discloses a social media platform interactive data display device 30 based on Tab switching, comprising:

[0167] An acquisition module 301 uses an API building tool to periodically acquire social media user interaction data DJ, performs data preprocessing on the interaction data DJ to obtain standard data DB, and stores the standard data DB in a database;

[0168] The calculation module 302 performs eigenvalue calculation on the standard data stored in the database according to the pre-built m eigenvalue calculation functions, and obtains a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB;

[0169] The arrangement module 303 obtains the standard time interval Tq, arranges the feature vectors in the feature vector set M in the time interval in time order, obtains a time series vector set M'={M'1, M'2, ...M'm}, performs time series feature calculation on the time series vector set M', and obtains a feature vector K={K1, K2, ...Km} containing m feature values;

[0170] The screening module 304 performs clustering calculation on the standard data DB to obtain a clustering vector J={J1, J2, ... Jn} containing n clustering points, calculates the matching value Pz=Fp(J, K) between the clustering vector J and the eigenvector K through the matching calculation function Fp, and screens out the eigenvalues ​​Ki whose matching values ​​are greater than the preset value P0, forming a screening vector K'={K'1, K'2, ... K'p} containing p eigenvalues, p≤m;

[0171] The display module 305 constructs p data display components of different styles for the screening vector K', and the p data display components of different styles display the standard data DB in p paging pages by means of Tab switching.

[0172] See also Figure 4 The embodiment of the present invention further provides an electronic device 60, the electronic device comprising:

[0173] at least one processor; and,

[0174] a memory communicatively connected to the at least one processor; wherein,

[0175] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the social media platform interactive data display method based on Tab switching in the aforementioned method embodiment.

[0176] An embodiment of the present invention further provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the aforementioned method embodiment.

[0177] An embodiment of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the social media platform interactive data display method based on Tab switching in the aforementioned method embodiment.

[0178] Reference below Figure 4, which shows a schematic diagram of the structure of an electronic device 60 suitable for implementing the embodiment of the present disclosure. The electronic device in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0179] like Figure 4 As shown, the electronic device 60 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 60 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0180] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, a gage, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 60 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 60 is shown with various devices, but it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have instead.

[0181] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 609, or installed from a storage device 608, or installed from a ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0182] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for displaying interactive data on a social media platform based on Tab switching, characterized in that: include: Using an API building tool to regularly obtain social media user interaction data DJ, pre-processing the interaction data DJ to obtain standard data DB, and storing the standard data DB in a database; According to the pre-constructed m eigenvalue calculation functions, eigenvalue calculation is performed on the standard data stored in the database to obtain a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB; Obtain the standard time interval Tq, arrange the feature vectors in the feature vector set M in time order within the time interval to obtain a time series vector set M'={M'1,M'2,...M'm}, perform time series feature calculation on the time series vector set M' to obtain a feature vector K={K1,K2,...Km} containing m feature values; Perform clustering calculation on the standard data DB to obtain a clustering vector J={J1,J2,…Jn} containing n clustering points, calculate the matching value Pz=Fp(J,K) between the clustering vector J and the eigenvector K through the matching calculation function Fp, filter out the eigenvalues ​​Ki whose matching values ​​are greater than the preset value P0, and form a screening vector K'={K'1,K'2,…K'p} containing p eigenvalues, p≤m; P data display components of different styles are constructed for the screening vector K', and the p data display components of different styles display the standard data DB in p pagination pages by means of Tab switching.

2. The method according to claim 1, characterized in that The data preprocessing of the interactive data DJ to obtain standard data DB includes: Calculate the mean of attribute A in the interaction data as , is the non-missing value of the attribute A, is the number of non-missing values, Fill missing values ​​in interaction data; Assume Q1 and Q3 are the lower quartile and upper quartile of the interaction data, respectively, set IQR = Q3-Q1, and regard data less than Q1-1.5×IQR or greater than Q3+1.5×IQR as outliers and correct or delete them.

3. The method according to claim 2, characterized in that The performing data preprocessing on the interactive data DJ to obtain standard data DB also includes: For the numerical data in the interactive data, let the mean of the data x be μ and the standard deviation be σ, then the standardized data x'= ; Encode categorical data in interaction data, mapping each category into a unique binary vector.

4. The method according to claim 3, characterized in that According to the pre-constructed m eigenvalue calculation functions, eigenvalue calculation is performed on the standard data stored in the database to obtain a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB, including: For each eigenvalue calculation function fi (i=1,2,...,m), operate on the relevant data fields in the standard data DB. Suppose the data set involved in the calculation in the standard data DB is D, and each eigenvector Mi is calculated by Mi=fi(D), where Mi contains the calculation result of the corresponding eigenvalue, thereby forming a eigenvector set M={M1,M2,...Mm}.

5. The method according to claim 4, characterized in that The method further comprises: performing eigenvalue calculation on the standard data stored in the database according to the pre-constructed m eigenvalue calculation functions to obtain a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB; When calculating each eigenvector Mi, record the intermediate parameters and statistical information during the calculation process; Perform a preliminary quality assessment on the calculated feature vector set M to check whether there are abnormal or unreasonable feature values. If so, adjust or recalculate them.

6. The method according to claim 5, characterized in that The feature vectors in the feature vector set M are arranged in time order within the time interval to obtain a time series vector set M'={M'1, M'2, ...M'm}, and the time series feature calculation is performed on the time series vector set M' to obtain a feature vector K={K1, K2, ...Km} containing m feature values, including: Determine the start time t1 and end time t2 of the time interval, extract the feature vectors in the corresponding time interval from the feature vector set M, and arrange them in chronological order to form a time series vector set M'; set up is the value of the time series at time t, is the autoregression coefficient, is the moving average coefficient, is a white noise sequence, p is the autoregressive order, q is the moving average order, then Calculate M' and obtain the eigenvector K={K1,K2,...Km} containing m eigenvalues.

7. The method according to claim 6, characterized in that The clustering calculation is performed on the standard data DB to obtain a clustering vector J={J1, J2, ... Jn} containing n clustering points, and the matching value Pz=Fp(J, K) between the clustering vector J and the eigenvector K is calculated by the matching calculation function Fp, and the eigenvalue Ki with a matching value greater than a preset value P0 is screened out to form a screening vector K'={K'1, K'2, ... K'p} containing p eigenvalues. include: Assume that the initial values ​​of cluster centers are C1, C2, ..., Cn, and perform iterative calculations on the data points in the standard data DB so that each data point is assigned to the nearest cluster center. After multiple iterations, stable n cluster points are obtained to form a cluster vector J = {J1, J2, ...Jn}; The matching value Pz between J and K is calculated through the matching calculation function Fp, which is determined according to specific business requirements and data characteristics; Compare Pz with the preset value P0, filter out the eigenvalue Ki where Pz>P0, and form a screening vector K'={K'1,K'2,...K'p}.

8. The method according to claim 7, characterized in that The step of constructing p data display components of different styles for the screening vector K', wherein the p data display components of different styles display the standard data DB in p paginated pages by means of Tab switching, includes: For each eigenvalue K'i (i=1,2,...,p), design a corresponding data display style according to its data characteristics and business needs; Using the front-end framework, create p data display components, each corresponding to a characteristic value K'i, and place these components on p pagination pages respectively; By setting the Tab switching function, users can switch between different paging pages to view the display of standard data DB related to different feature values.

9. A device for displaying interactive data on a social media platform based on Tab switching, characterized in that: include: An acquisition module, using an API construction tool to regularly acquire social media user interaction data DJ, preprocessing the interaction data DJ to obtain standard data DB, and storing the standard data DB in a database; A calculation module performs eigenvalue calculation on the standard data stored in the database according to the pre-built m eigenvalue calculation functions, and obtains a set of m eigenvectors M={M1, M2, ...Mm} related to the standard data DB; an arrangement module, obtaining the standard time interval Tq, arranging the feature vectors in the feature vector set M in the time interval in chronological order to obtain a time series vector set M'={M'1,M'2,...M'm}, performing time series feature calculation on the time series vector set M' to obtain a feature vector K={K1,K2,...Km} containing m feature values; The screening module performs clustering calculation on the standard data DB to obtain a clustering vector J={J1,J2,…Jn} containing n clustering points, calculates the matching value Pz=Fp(J,K) between the clustering vector J and the eigenvector K through the matching calculation function Fp, and screens out the eigenvalues ​​Ki whose matching values ​​are greater than the preset value P0 to form a screening vector K'={K'1,K'2,…K'p} containing p eigenvalues, where p≤m; The display module constructs p data display components of different styles for the screening vector K', and the p data display components of different styles display the standard data DB in p pagination pages by means of Tab switching.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the social media platform interactive data display method based on Tab switching as described in any one of claims 1 to 8.

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