A system and method for constructing a multi-source data analysis process

Abstract

The present invention discloses a system and method for constructing a multi-source data analysis process, and relates to the technical field of multi-source data analysis optimization. The method comprises the following steps: multi-source data collection based on the Internet, including web page data crawling, social media data collection, and public database data extraction, by establishing a multi-source analysis database; matching related data based on the multi-source analysis database according to the keywords input by the user, performing correlation analysis on the related data, arranging in descending order based on the correlation, and giving priority to displaying matching data with high correlation; classifying the matched related data, and combining the content displayed for the first time and the user's subsequent operations. The present invention enhances practicality and functionality by interpreting and classifying the keywords input by the user, obtaining related data in combination with the multi-source analysis database, and marking them in combination with the interpretation categories, and dynamically adjusting the displayed data based on the user's access behavior.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-source data analysis optimization, and in particular to a system and method for constructing a multi-source data analysis process. Background Art

[0002] The multi-source data analysis process refers to the process of collecting data from multiple different data sources during a search process, matching it with the search keywords, and displaying relevant results to users based on the similarity of keyword matching;

[0003] The existing multi-source data analysis and matching processes are mostly based on the similarity sorting of user keywords to display and sort the related data. The display order is fixed, that is, the display data on each page is fixed. It is impossible to dynamically display and adjust the matching data according to the user's actual operation, making it difficult to assist users in quickly obtaining the required data, and there are problems with low practicality and functionality.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related art, the present invention proposes a system and method for constructing a multi-source data analysis process to overcome the above-mentioned technical problems existing in the existing related art.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] A method for constructing a multi-source data analysis process, the method comprising the following steps:

[0008] S1. Collect multi-source data based on the Internet, including web page data crawling, social media data collection, and public database data extraction. Establish a multi-source analysis database;

[0009] S2. Match related data based on the multi-source analysis database according to the keywords input by the user, perform relevance analysis on the related data, sort them in descending order based on the relevance, and give priority to displaying matching data with high relevance;

[0010] S3. Categorize the matching related data, optimize the display strategy for the matching data that has not been displayed based on the content displayed for the first time and the user's subsequent operations, and dynamically adjust the displayed content.

[0011] As a preferred embodiment, S3 includes the following sub-steps:

[0012] S31, based on each keyword search by the user Establish a display database, analyze the associated data matched by user keywords in the multi-source database, classify the associated data into categories based on the interpretation of the keywords and the content of the associated data, establish category files in the display database, and match the associated data of the same category into the same file;

[0013] S32. For the data displayed for the first time based on the correlation matching, dynamically adjust the content displayed subsequently in combination with the user's access behavior and the category of the matching data.

[0014] As a preferred implementation, the S31 includes the following sub-steps:

[0015] S311, for the associated data based on the user keyword matching in the multi-source analysis database, combined with the interpretation of the keyword, the corresponding interpretation of the keyword is analyzed for the content of the obtained associated data, the obtained associated data is divided into interpretation categories, and the interpretation categories of different associated data are determined. The specific steps are:

[0016] pass Search the keyword input by the user to obtain one or more synonym sets corresponding to the current keyword, each synonym set includes the interpretation content of the concept corresponding to the keyword, including associated words and interpretation descriptions;

[0017] pass The algorithm extracts text keywords from the associated data matched by user keywords. The specific steps are as follows:

[0018] ;

[0019] ;

[0020] in, is the total number of documents, that is, the total number of associated data, Represents the word The number of documents based on and Calculated :

[0021] ;

[0022] based on The value is used to extract the current document ranking The words are used as keywords of the current document;

[0023] S312, calculating the similarity between the keyword vector extracted from the associated data and the vector set corresponding to each interpretation of the user keyword by cosine similarity, so as to determine the category of the current associated data, the steps are as follows:

[0024] pass The keywords extracted from the associated data and the user keywords are converted into vectors of fixed dimensions, and the cosine similarity is calculated. The algorithm formula is:

[0025] ;

[0026] in, They represent the keyword vectors extracted from the associated data and the user keyword vectors respectively. Represents the cosine similarity under different interpretations;

[0027] Based on similarity threshold , when a certain interpretation keyword > , it means that the current associated data belongs to the current interpretation category.

[0028] As a preferred implementation, the S312 further includes the following steps:

[0029] S3121, after each keyword search by the user, based on Establish a display database and create category files based on the interpretation of user keywords;

[0030] S3122. According to the interpretation categories divided by the cosine similarity of the associated data, the corresponding associated data are divided into the established corresponding category files.

[0031] As a preferred embodiment, the S32 includes the following sub-steps:

[0032] S321, counting the total number of data to be displayed first, obtaining the user's access behavior, and separately marking the associated data clicked by the user and the associated data not accessed by the user;

[0033] S322. Determine the category of the associated data of the tags according to the associated data of different user behavior tags and in combination with the category archive, and dynamically adjust the subsequent display content.

[0034] As a preferred implementation, the S322 includes the following sub-steps:

[0035] S3221. For the associated data accessed by the user, in combination with the category archive, the category of the associated data accessed is traced, the category of the associated data accessed is determined, and the current category is marked green;

[0036] S3222. For the associated data that the user has not accessed, in combination with the category archive, the category of the associated data that has not been accessed is traced, the category of the associated data that has not been accessed is determined, and the current category is marked in red;

[0037] S3223, mark the unmarked category files with yellow marks, and dynamically adjust the subsequent display content. The specific steps are:

[0038] The original descending order of relevance is screened out, the red-marked category-related data in the original descending order of relevance and the data displayed for the first time based on relevance matching are removed, and the updated descending series is marked with a green-yellow ratio of 6:4 based on the category, and the related data is extracted. The specific steps are:

[0039] ;

[0040] ;

[0041] in, Represents the total number of single impression data. Respectively represent the number of green related data to be extracted and the number of yellow related data to be extracted. In the updated descending sequence, extract from the green category records in descending order of relevance. Related data are extracted from the yellow category records in descending order of relevance Items of related data are displayed;

[0042] Each time a user ends accessing the current page, the access behavior is recorded, the remaining green and yellow mark categories are re-marked in combination with the current user's access behavior, and subsequent display pages are adjusted.

[0043] As a preferred embodiment, S2 comprises the following steps:

[0044] S21. Preprocess the keywords input by the user through the Chinese word segmentation tool Segment the keywords entered by the user and remove stop words;

[0045] S22, based on fuzzy matching algorithm, through The distance formula performs fuzzy matching of keyword stems on the data in the multi-source analysis database, matches the associated data from the multi-source analysis database and records the data links;

[0046] S23, through Convert the text in the user keywords and matching associated data into word vector representations. At the same time, for user keywords and matching data, convert the word vectors in the text into word vector representations. Weighted averaging to generate a vector representation of the text;

[0047] S24. For each matching associated data, the cosine similarity between the text vector of the matching associated data and the text vector of the user keyword is calculated, and the matching data are arranged in descending order based on the cosine similarity, and the data with the highest ranking are displayed first.

[0048] A system for constructing a multi-source data analysis process, including a multi-source data acquisition module, a user information input module, a user information matching module, and a display optimization module:

[0049] The multi-source data collection module includes crawler software, which collects multi-source data based on the Internet, including web page data crawling, social media data collection, and public database data extraction. Establish a multi-source analysis database;

[0050] The user information input module is used to receive keyword information input by the user, and pre-process the keyword information input by the user, including word segmentation and removal of stop words, and transmit the processed user information text to the user information matching module;

[0051] The user information matching module, based on the multi-source analysis database and the user information text, obtains the associated data of the current user information text from the multi-source analysis database through a fuzzy matching algorithm, calculates the degree of association, and arranges the associated data in descending order based on the degree of association in combination with the data volume limit for page display, and selects the associated data that meets the data volume limit for the first display in turn;

[0052] The display optimization module marks the data accessed by the user, the data not accessed and the data not displayed respectively based on the interpretation of the user's keywords and the user's access behavior, and dynamically adjusts the subsequent display content.

[0053] The beneficial effects of the present invention are:

[0054] 1. The present invention identifies the meaning of the keywords input by the user, matches the keyword-related data in the multi-source analysis database, classifies the keywords based on their meanings, and determines the meanings of the keywords that the user is interested in based on the user's access behavior, so as to improve the accuracy of user intention identification;

[0055] 2. The present invention marks the data accessed by the user, the data not accessed and the data not displayed respectively based on the user's access behavior, determines the data categories under different marks, and adjusts the subsequent display strategy, that is, does not display the interpretation categories of the associated data that the user has not accessed, and dynamically adjusts the associated data of the interpretation categories of the user accessed data and the associated data of the undisplayed interpretation categories in the subsequent pages according to the set ratio combined with the correlation ranking, so that the matching results are closer to the actual needs of the user, and the adaptability and practicality are improved.

[0056] 3. The present invention re-marks the updated associated data each time the user ends accessing the current page, dynamically adjusts the subsequent display data based on the user's access behavior, gradually narrows the access scope of the keyword interpretations entered by the user, eliminates the interpretation categories that the user is not interested in, optimizes the allocation of display resources, gradually narrows the access scope, and improves the pertinence of the displayed content. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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.

[0058] Figure 1 is a flowchart of a method for constructing a multi-source data analysis process according to an embodiment of the present invention;

[0059] Figure 2 It is a system block diagram for constructing a multi-source data analysis process according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0061] According to an embodiment of the present invention, a system and method for constructing a multi-source data analysis process are provided.

[0062] The present invention is further described with reference to the accompanying drawings and specific embodiments:

[0063] Example 1: Figure 1 As shown, a method for constructing a multi-source data analysis process according to an embodiment of the present invention includes the following steps:

[0064] S1. Collect multi-source data based on the Internet, including web page data crawling, social media data collection, and public database data extraction. Establish a multi-source analysis database;

[0065] S2. Match related data based on the multi-source analysis database according to the keywords input by the user, perform relevance analysis on the related data, sort them in descending order based on the relevance, and give priority to displaying matching data with high relevance;

[0066] S21. Preprocess the keywords input by the user through the Chinese word segmentation tool Segment the keywords entered by the user and remove stop words;

[0067] S22, based on fuzzy matching algorithm, through The distance formula performs fuzzy matching of keyword stems on the data in the multi-source analysis database, matches the associated data from the multi-source analysis database and records the data links;

[0068] S23, through Convert the text in the user keywords and matching associated data into word vector representations. At the same time, for user keywords and matching data, convert the word vectors in the text into word vector representations. Weighted averaging to generate a vector representation of the text;

[0069] S24. For each matching associated data, the cosine similarity between the text vector of the matching associated data and the text vector of the user keyword is calculated, and the matching data are arranged in descending order based on the cosine similarity, and the data with the highest ranking are displayed first.

[0070] It should be noted that the matching data is sorted according to the calculated cosine similarity. The higher the cosine similarity, the stronger the correlation between the matching data and the user's keyword.

[0071] Embodiment 2: S3, classifying the matching related data into categories, optimizing the display strategy for the matching data that has not been displayed, and dynamically adjusting the display content based on the content displayed for the first time and the user's subsequent operations;

[0072] S31, based on each keyword search by the user Establish a display database, analyze the associated data matched by user keywords in the multi-source database, classify the associated data into categories based on the interpretation of the keywords and the content of the associated data, establish category files in the display database, and match the associated data of the same category into the same file;

[0073] S311, for the associated data based on the user keyword matching in the multi-source analysis database, combined with the interpretation of the keyword, the corresponding interpretation of the keyword is analyzed for the content of the obtained associated data, the obtained associated data is divided into interpretation categories, and the interpretation categories of different associated data are determined. The specific steps are:

[0074] pass Search the keyword input by the user to obtain one or more synonym sets corresponding to the current keyword, each synonym set includes the interpretation content of the concept corresponding to the keyword, including associated words and interpretation descriptions;

[0075] pass The algorithm extracts text keywords from the associated data matched by user keywords. The specific steps are as follows:

[0076] ;

[0077] ;

[0078] in, is the total number of documents, that is, the total number of associated data, Represents the word The number of documents based on and Calculated :

[0079] ;

[0080] based on The value is used to extract the current document ranking The words are used as keywords of the current document;

[0081] It should be noted that based on The value is used to extract the current document ranking The words are used as keywords of the current document, where The value is usually 3. Take the user's keyword "apple" as an example. Get the definitions of "Apple" and "fruit", where the definitions of "Apple" include "electronic products", "mobile phone", "watch", etc., and the definitions of "fruit" include "maturity", "nutritional value", etc. Extract the top three keywords in the associated data to match the interpretation content of the corresponding user keywords to determine the category of the current associated data;

[0082] S312, calculating the similarity between the keyword vector extracted from the associated data and the vector set corresponding to each interpretation of the user keyword by cosine similarity, so as to determine the category of the current associated data, the steps are as follows:

[0083] pass The keywords extracted from the associated data and the user keywords are converted into vectors of fixed dimensions, and the cosine similarity is calculated. The algorithm formula is:

[0084] ;

[0085] in, They represent the keyword vectors extracted from the associated data and the user keyword vectors respectively. Represents the cosine similarity under different interpretations;

[0086] Based on similarity threshold , when a certain interpretation keyword > , it means that the current associated data belongs to the current interpretation category.

[0087] It should be noted that the cosine similarity value range is between -1 and 1. The higher the cosine similarity value, the more similar the semantics. That is, the higher the similarity, the lower the similarity threshold. It is usually set to 0.5, that is, if the cosine similarity of the associated data under a certain interpretation is greater than 0.5, it means that the current associated data belongs to the current interpretation category.

[0088] S3121, after each keyword search by the user, based on Establish a display database and create category files based on the interpretation of user keywords;

[0089] S3122, according to the interpretation categories divided by the cosine similarity of the associated data, the corresponding associated data are divided into established corresponding category files;

[0090] S32, dynamically adjusting the content to be subsequently displayed for the data first displayed based on the correlation matching, in combination with the user's access behavior and the category of the matching data;

[0091] S321, counting the total number of data to be displayed first, obtaining the user's access behavior, and separately marking the associated data clicked by the user and the associated data not accessed by the user;

[0092] S322, according to the associated data of different user behavior tags, combined with the category archive, determine the category of the associated data of the tag, and dynamically adjust the subsequent display content;

[0093] S3221. For the associated data accessed by the user, in combination with the category archive, the category of the associated data accessed is traced, the category of the associated data accessed is determined, and the current category is marked green;

[0094] S3222. For the associated data that the user has not accessed, in combination with the category archive, the category of the associated data that has not been accessed is traced, the category of the associated data that has not been accessed is determined, and the current category is marked in red;

[0095] S3223, mark the unmarked category files with yellow marks, and dynamically adjust the subsequent display content. The specific steps are:

[0096] The original descending order of relevance is screened out, the red-marked category-related data in the original descending order of relevance and the data displayed for the first time based on relevance matching are removed, and the updated descending series is marked with a green-yellow ratio of 6:4 based on the category, and the related data is extracted. The specific steps are:

[0097] ;

[0098] ;

[0099] in, Represents the total number of single impression data. Respectively represent the number of green related data to be extracted and the number of yellow related data to be extracted. In the updated descending sequence, extract from the green category records in descending order of relevance. Related data are extracted from the yellow category records in descending order of relevance Items of related data are displayed;

[0100] Each time a user ends accessing the current page, the access behavior is recorded, the remaining green and yellow mark categories are re-marked in combination with the current user's access behavior, and subsequent display pages are adjusted.

[0101] It should be noted that by marking the visited related data categories with green based on the user's access behavior, it means that the user is interested in the related data under the current keyword interpretation, which increases the proportion of current related data on subsequent pages, and removes the related data under the keyword interpretation that the user has not visited to prevent the user from contacting the relevant data again. At the same time, the interpretation data that has not been displayed continues to be displayed to gradually narrow the user's access scope.

[0102] A system for constructing a multi-source data analysis process, including a multi-source data acquisition module, a user information input module, a user information matching module, and a display optimization module:

[0103] The multi-source data collection module includes crawler software, which collects multi-source data based on the Internet, including web page data crawling, social media data collection, and public database data extraction. Establish a multi-source analysis database;

[0104] The user information input module is used to receive the keyword information input by the user, and pre-process the keyword information input by the user, including word segmentation and removal of stop words, and transmit the processed user information text to the user information matching module;

[0105] The user information matching module, based on the multi-source analysis database and the user information text, obtains the associated data of the current user information text from the multi-source analysis database through a fuzzy matching algorithm, calculates the degree of association, and arranges the associated data in descending order based on the degree of association in combination with the data volume limit displayed on the page, and selects the associated data that meets the data volume limit for the first display;

[0106] The display optimization module marks the data accessed by the user, the data not accessed, and the data not displayed separately based on the interpretation of the user's keywords and the user's access behavior, and dynamically adjusts the subsequent display content

[0107] In summary, the present invention identifies the meaning of the keywords input by the user, matches the keyword-related data in the multi-source analysis database, classifies the keywords based on their meanings, and determines the keyword meanings that the user is interested in based on the user's access behavior, so as to improve the accuracy of user intent identification;

[0108] Based on the user's access behavior, the data accessed by the user, the data not accessed, and the data not displayed are marked separately, and the data categories under different marks are determined, and the subsequent display strategy is adjusted, that is, the interpretation categories of the related data that the user has not accessed are not displayed, and the interpretation category associated data of the user accessed data and the associated data of the non-displayed interpretation category in the subsequent pages are dynamically adjusted according to the set ratio and the relevance sorting, so that the matching results are closer to the actual needs of the users.

[0109] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a multi-source data analysis process, characterized in that: The method comprises the following steps: S1. Collect multi-source data based on the Internet, including web page data crawling, social media data collection, and public database data extraction, and establish a multi-source analysis database through MySQL; S2. Match related data based on the multi-source analysis database according to the keywords input by the user, perform relevance analysis on the related data, sort them in descending order based on the relevance, and give priority to displaying matching data with high relevance; S3. Classify the matching related data, optimize the display strategy for the matching data that has not been displayed, and dynamically adjust the display content based on the content displayed for the first time and the user's subsequent operations; S31. According to each keyword search by the user, a display database is established based on MySQL. The associated data matching the user's keyword in the multi-source analysis database is classified according to the interpretation of the keyword and the content of the associated data. A category file is established in the display database, and the associated data of the same category are matched to the same file. S311, for the associated data based on the user keyword matching in the multi-source analysis database, combined with the interpretation of the keyword, the corresponding interpretation of the keyword is analyzed for the content of the obtained associated data, the obtained associated data is divided into interpretation categories, and the interpretation categories of different associated data are determined. The specific steps are: Search the keyword input by the user through WordNet to obtain one or more synonym sets corresponding to the current keyword. Each synonym set includes the interpretation content of the corresponding concept of the keyword, including associated words and interpretation descriptions; Through the TF-IDF algorithm, text keywords are extracted from the associated data matching the user's keywords. The specific steps are as follows: Where N is the total number of documents, that is, the total number of associated data, |{d∈D:t∈d}| represents the number of documents containing word t, and TF-IDF(t,d,D) is calculated based on TF(t,d) and IDF(t,D): TF-IDF(t,d,D)=TF(t,d)×IDF(t,D); Based on the TF-IDF value, the word ranked n in the current document is extracted as the keyword of the current document; S312, calculating the similarity between the keyword vector extracted from the associated data and the vector set corresponding to each interpretation of the user keyword by cosine similarity, so as to determine the category of the current associated data, the steps are as follows: The keywords extracted from the associated data and the user keywords are converted into vectors of fixed dimensions through GloVe, and the cosine similarity is calculated. The algorithm formula is: Among them, v1 and v2 represent the keyword vector extracted from the associated data and the user keyword vector respectively, and δ(v1,v2) represents the cosine similarity under different interpretations; Based on the similarity threshold θ, when δ(v1,v2)>θ under a certain interpretation keyword, it means that the current associated data belongs to the current interpretation category; S32, dynamically adjusting the content to be subsequently displayed for the data first displayed based on the correlation matching, in combination with the user's access behavior and the category of the matching data; S321, counting the total number of data to be displayed first, obtaining the user's access behavior, and separately marking the associated data clicked by the user and the associated data not accessed by the user; S322, according to the associated data of different user behavior tags, combined with the category archive, determine the category of the associated data of the tag, and dynamically adjust the subsequent display content; S3221. For the associated data accessed by the user, in combination with the category archive, the category of the associated data accessed is traced, the category of the associated data accessed is determined, and the current category is marked green; S3222. For the associated data that the user has not accessed, in combination with the category archive, the category of the associated data that has not been accessed is traced, the category of the associated data that has not been accessed is determined, and the current category is marked in red; S3223, mark the unmarked category files with yellow marks, and dynamically adjust the subsequent display content. The specific steps are: The original descending order of relevance is screened out, the red-marked category-related data in the original descending order of relevance and the data displayed for the first time based on relevance matching are removed, and the updated descending series is marked with a green-yellow ratio of 6:4 based on the category, and the related data is extracted. The specific steps are: Among them, s represents the total number of data displayed at a time, q and r represent the number of green related data to be extracted and the number of yellow related data to be extracted respectively. In the updated descending sequence, q related data are extracted from the green category records in descending order of relevance, and r related data are extracted from the yellow category records in descending order of relevance for display; Each time a user ends accessing the current page, the access behavior is recorded, and the remaining green and yellow mark categories are re-marked in combination with the current user's access behavior. The subsequent display pages are adjusted continuously, and the data is displayed or eliminated based on the user's access behavior, gradually narrowing the user's access scope.

2. A method for constructing a multi-source data analysis process according to claim 1, characterized in that: The S312 further includes the following steps: S3121. After each keyword search by the user, a display database is established based on MySQL, and category files are established based on the interpretation of the user's keywords; S3122. According to the interpretation categories divided by the cosine similarity of the associated data, the corresponding associated data are divided into the established corresponding category files.

3. A method for constructing a multi-source data analysis process according to claim 1, characterized in that: The S2 comprises the following steps: S21, pre-processing the keywords input by the user, segmenting the keywords input by the user through the Chinese word segmentation tool Jieba, and removing stop words; S22. Based on the fuzzy matching algorithm, the Jaro-Winkler distance formula is used to perform fuzzy matching of keyword stems on the data in the multi-source analysis database, match the associated data from the multi-source analysis database and record the data links; S23, converting the user keywords and the text in the matching associated data into word vector representations through Word2Vec, and generating a vector representation of the text by weighted averaging the word vectors in the text through TF-IDF for the user keywords and the matching data; S24. For each matching associated data, the cosine similarity between the text vector of the matching associated data and the text vector of the user keyword is calculated, and the matching data are arranged in descending order based on the cosine similarity, and the data with the highest ranking are displayed first.

4. A system for constructing a multi-source data analysis process, characterized in that: The method adopts the method for constructing a multi-source data analysis process as described in any one of claims 1 to 3, including a multi-source data acquisition module, a user information input module, a user information matching module, and a display optimization module: The multi-source data collection module includes crawler software, which performs multi-source data collection based on the Internet, including web page data crawling, social media data collection, and public database data extraction, and establishes a multi-source analysis database through MySQL; The user information input module is used to receive keyword information input by the user, and pre-process the keyword information input by the user, including word segmentation and removal of stop words, and transmit the processed user information text to the user information matching module; The user information matching module, based on the multi-source analysis database and the user information text, obtains the associated data of the current user information text from the multi-source analysis database through a fuzzy matching algorithm, calculates the degree of association, and arranges the associated data in descending order based on the degree of association in combination with the data volume limit for page display, and selects the associated data that meets the data volume limit for the first display in turn; The display optimization module marks the data accessed by the user, the data not accessed and the data not displayed respectively based on the interpretation of the user's keywords and the user's access behavior, and dynamically adjusts the subsequent display content.

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