Intelligent content display method and system based on data integrity

Abstract

The invention provides an intelligent content display method and system based on data integrity, and relates to the technical field of mobile internet, and the method comprises the steps: obtaining global content data, carrying out the structural processing of a plurality of data items contained in the global content data, and representing each data item as a set of a group of feature attributes; performing data distribution analysis based on the feature attribute set to construct a structured data distribution structure; and in the data distribution structure, according to a preset data feature boundary, dividing a plurality of different data representation intervals, and classifying each data item into the corresponding data representation interval to obtain each classified data representation interval. According to the invention, user experience optimization of data integrity judgment, rapid loading and smooth switching is realized.

Description

Technical Field

[0001] This invention relates to the field of mobile internet technology, and in particular to a method and system for intelligent content display based on data integrity. Background Technology

[0002] With the development of mobile internet, applications that rely on user-generated content, such as social networking, memory-based, and photo album applications, are gradually increasing. The core requirement of these applications is to organize fragmented content into a coherent display format based on the data actually accumulated by users. However, existing technologies are difficult to meet this requirement. Current mainstream solutions mostly adopt predefined fixed page structures or handle data loss issues through conditional rendering within a fixed page framework. That is, a fixed number of pages and display order are set in advance, and only some content within the page is hidden or a placeholder prompt is displayed when data is missing. This does not fundamentally and dynamically adjust the page display logic.

[0003] For example, consider a memory-based app. To present a user's emotional journey, such apps predefine several fixed pages, including meeting records, daily dating moments, anniversary summaries, and shared photo albums. During the page loading phase, the app first fully loads the frames of all predefined pages, then checks each page for the existence of corresponding data. If data is missing, a placeholder prompt appears on the page indicating that there is currently no data and users should go and add it. Users need to swipe through all the page frames to see the content with data. This approach has obvious technical flaws: even if some pages have no valid data, the frames of these pages are still loaded and retained. Users have to repeatedly swipe to skip meaningless pages with only placeholder prompts, disrupting the continuity of content display and failing to create an immersive browsing experience. This makes it difficult for users to smoothly perceive the core value of the app's memory function. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an intelligent content display method and system based on data integrity, which realizes user experience optimization such as data integrity judgment, fast loading and smooth switching.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a method for intelligent content display based on data integrity, the method comprising: Obtain global content data, perform structured processing on multiple data items contained in the global content data, and represent each data item as a set of feature attributes; Based on the set of feature attributes, data distribution analysis is performed to construct a structured data distribution structure. In the data distribution structure, multiple different data representation intervals are divided according to the preset data feature boundaries, and each data item is classified into the corresponding data representation interval to obtain each classified data representation interval. Based on the data items within each data representation interval after classification, a point set is constructed. By solving the centroid coordinates of the point set within each interval, the aggregation state of the data items is quantified using the centroid coordinates of the point set. An evaluation parameter for comprehensively evaluating data integrity is calculated. The global content data and the evaluation parameter are then input into the predefined rule engine. Based on the rule engine and global content data, all predefined display pages are traversed, and each display page is judged to meet the corresponding data integrity conditions according to the mapping relationship in order to obtain the mapping relationship judgment result. Based on the mapping relationship, a target page sequence that satisfies the data integrity condition is dynamically generated. Based on the target page sequence, intelligent content display is achieved by loading and rendering the target page sequence to ensure the integrity of user data.

[0006] Furthermore, the global content data is obtained, and the multiple data items contained within the global content data are structured, representing each data item as a set of feature attributes, including: Obtain the user's original content data, perform structured cleaning and normalization on the original content data, and unify the heterogeneous data format into a standard data object; Based on a predefined feature attribute rule base, features are extracted for each standard data object to generate a corresponding feature attribute set; the feature attribute set includes at least one attribute among data type, creation timestamp, data size, number of associated entities, and content quality score.

[0007] Furthermore, based on the set of feature attributes, data distribution analysis is performed to construct a structured data distribution structure. Within this structure, multiple distinct data representation intervals are defined according to predefined data feature boundaries. Each data item is then categorized into its corresponding data representation interval, resulting in each categorized data representation interval, including: Based on the set of feature attributes, determine one or more core analytical dimensions for data distribution analysis; Based on the determined core analysis dimensions, a multi-dimensional data distribution structure is constructed, and each data item is mapped to a coordinate point in the data distribution structure according to its feature attributes, so as to obtain the mapped coordinate point set. Based on the mapped set of coordinate points, and according to the preset data feature boundaries corresponding to each analysis dimension, multiple independent data representation intervals are divided in the data distribution structure. Based on the predefined data representation intervals, the coordinates of each data item are categorized into the corresponding data representation interval according to their spatial location.

[0008] Furthermore, based on the data items within each categorized data representation interval, a point set is constructed. By solving for the centroid coordinates of the point set within each interval, the aggregation state of the data items is quantified using the centroid coordinates of the point set, and an evaluation parameter for comprehensively assessing data integrity is calculated. The global content data and the evaluation parameter are then input into a predefined rule engine, including: Based on each data representation interval after classification, the coordinate points corresponding to all data items within the interval are extracted to construct an independent set of coordinate points for each interval; For each set of coordinate points constructed within a data representation interval, the centroid coordinates of the data representation interval point set are obtained by calculating the geometric center position. Based on the centroid coordinates of each interval obtained by the solution, and combined with the number of data items contained in each interval, the overall clustering state and distribution density of all data items in the data distribution structure are quantified. Based on the overall aggregation state and distribution density, evaluation parameters for comprehensively assessing data integrity are calculated using preset weighted calculation rules. The global content data and the calculated evaluation parameters are input into the predefined rule engine.

[0009] Furthermore, based on the rule engine and global content data, all predefined display pages are traversed, and each display page is judged to meet the corresponding data integrity conditions according to the mapping relationship, so as to obtain the mapping relationship judgment result; including: Based on the input global content data and the evaluation parameters, the rule engine is initialized, and all predefined display pages and corresponding data integrity conditions are loaded. Based on the already loaded collection of display pages, each display page is traversed sequentially using the rule engine; During the traversal, for the currently traversed display page, based on the global content data and evaluation parameters, a matching judgment is made on the corresponding data integrity conditions to obtain the matching judgment result; Based on the matching results, record whether the traversed display pages meet the display conditions, and update the mapping relationship judgment result set; After traversing and judging all displayed pages, output the final mapping relationship judgment result.

[0010] Furthermore, based on the mapping relationship, a target page sequence that satisfies the data integrity requirements is dynamically generated, including: Read the mapping relationship judgment results, identify all display pages that meet the data integrity conditions, and form a candidate page set; Based on evaluation parameters and preset page priority rules, the display pages in the candidate page set are dynamically sorted. Based on the dynamic sorting results, the candidate pages are combined in order to obtain the final target page sequence.

[0011] Furthermore, based on the target page sequence, intelligent content display ensuring user data integrity is achieved by loading and rendering the target page sequence, including: Receive the target page sequence, obtain page configuration information by parsing the target page sequence, and initialize the page rendering environment based on the parsed configuration information; Based on the initialized rendering environment, a lazy loading strategy is adopted to load and render the page content currently located in the visible area of ​​the target page sequence. After the initial page rendering is completed, the swipe switching operation in the page sequence triggers the preloading request of adjacent pages based on the swipe direction and distance; Based on the preloading request, the content resources of the adjacent pages are dynamically loaded. At the same time, according to the changes of the currently active pages, resource reclamation operations are performed on the pages that have been moved out of the visible area. Based on the current rendering state and page switching actions, the associated transition animation effects are automatically triggered to complete the intelligent content display that ensures the integrity of user data.

[0012] Secondly, an intelligent content display system based on data integrity includes: The acquisition module is used to acquire global content data, perform structured processing on multiple data items contained in the global content data, and represent each data item as a set of feature attributes; The evaluation module performs data distribution analysis based on a set of feature attributes to construct a structured data distribution structure. Within this structure, multiple distinct data representation intervals are defined according to preset data feature boundaries. Each data item is then categorized into its corresponding interval, resulting in a categorized data representation interval. A point set is constructed based on the data items within each interval. By calculating the centroid coordinates of the point set within each interval, the aggregation state of the data items is quantified using these coordinates, and an evaluation parameter is calculated to comprehensively assess data integrity. The global content data and the evaluation parameter are then input into a predefined rule engine. The processing module is used to traverse all predefined display pages based on the rule engine and global content data, determine whether each display page meets the corresponding data integrity conditions according to the mapping relationship, and obtain the mapping relationship judgment result; based on the mapping relationship judgment result, dynamically generate a target page sequence of display pages that meet the data integrity conditions; based on the target page sequence, load and render the target page sequence to complete the intelligent content display of user data integrity.

[0013] Thirdly, a computing device includes: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0014] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0015] The above-described solution of the present invention has at least the following beneficial effects: This invention employs structured cleaning and normalization of global content data, extracting a set of feature attributes including data type, creation timestamp, and data size. Based on these feature attributes, a multi-dimensional data distribution structure is constructed, and data representation intervals are divided. By solving for the centroid coordinates of each interval's point set, the data aggregation state is quantified to calculate comprehensive evaluation parameters. The rule engine combines global content data and evaluation parameters to traverse and determine the completeness of predefined display pages. Based on the evaluation parameters and page priority rules, a target page sequence is dynamically generated. This involves lazy loading of visible area pages, preloading adjacent pages during scrolling, and reclaiming resources from pages moved out of the visible area. The new rendering strategy automatically triggers transition animations during page switching, overcoming the shortcomings of existing technologies such as inaccurate integrity judgment due to data heterogeneity, reliance on qualitative judgments lacking precise quantitative basis for data integrity, blank pages, long initial loading time and high memory consumption, fixed page sequences without personalization, and disjointed user experience due to lack of smooth page transitions. It achieves more accurate data integrity judgment, provides objective quantitative support for page selection based on evaluation parameters, completely eliminates blank pages, reduces initial loading time and memory consumption, generates personalized target page sequences that fit user data characteristics, and improves the smoothness of page switching and the user's immersive browsing experience. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an intelligent content display method based on data integrity, provided in an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of an intelligent content display system based on data integrity provided in an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0019] like Figure 1 As shown, embodiments of the present invention propose an intelligent content display method based on data integrity, the method comprising the following steps: Step 1: Obtain global content data, perform structured processing on the multiple data items contained in the global content data, and represent each data item as a set of feature attributes; Step 2: Based on the set of feature attributes, perform data distribution analysis to construct a structured data distribution structure; in the data distribution structure, according to the preset data feature boundaries, divide multiple different data representation intervals, and classify each data item into the corresponding data representation interval to obtain each classified data representation interval; Step 3: Construct a point set based on the data items within each data representation interval after classification. Quantify the aggregation state of the data items using the centroid coordinates of the point set within each interval, and calculate an evaluation parameter for comprehensively assessing data integrity. Input the global content data and the evaluation parameter together into the predefined rule engine. Step 4: Based on the rule engine and global content data, traverse all predefined display pages and determine whether each display page meets the corresponding data integrity conditions according to the mapping relationship to obtain the mapping relationship judgment result; Step 5: Based on the mapping relationship, dynamically generate a target page sequence that satisfies the data integrity conditions for display pages; Step 6: Based on the target page sequence, load and render the target page sequence to complete the intelligent content display for user data integrity.

[0020] In this embodiment of the invention, because the invention performs structured processing on multiple data items in the global content data and represents each data item as a set of feature attributes, constructs a structured data distribution structure based on the set of feature attributes, divides data representation intervals and classifies data items, and quantifies the data aggregation state by solving the centroid coordinates of the data item point set in each interval to calculate the evaluation parameters for comprehensively evaluating data integrity, the global content data and evaluation parameters are input into a predefined rule engine to traverse and judge the integrity of all predefined display pages, and then dynamically generates a target page sequence that meets the data integrity conditions based on the judgment results and loads and renders it, the invention overcomes the problems of inaccurate integrity judgment due to heterogeneous data formats, lack of quantitative basis to support data integrity assessment, and blank pages and insufficient personalization in the existing technology, which cannot be dynamically adjusted according to data integrity. The invention achieves more accurate data integrity assessment, completely eliminates blank pages, realizes personalized target page sequence display that fits the user's data integrity, and improves the intelligence of content display and user browsing experience.

[0021] In a preferred embodiment of the present invention, step 1 above may include: Step 1.1: Obtain the user's raw content data. Perform structured cleaning and normalization on the raw content data to unify heterogeneous data formats into standard data objects. Specifically, this includes: obtaining the user's raw content data from memory-based applications. This raw content data covers various heterogeneous data accumulated by users within the application, such as text information recording the process of getting to know each other, mixed text and image content recording daily life in a relationship, date and text descriptions marking anniversaries, image files storing shared albums, and chat log fragments generated by user interactions. Perform structured cleaning on this raw content data, filtering and removing invalid data, such as damaged data that cannot be opened. Image files, empty text records, and duplicated anniversary data are all processed using a normalization process. This process converts data of different formats into a standard format that the application can recognize. For example, all date data is uniformly adjusted to the YYYY-MM-DD date format, image files of different resolutions are uniformly compressed or adjusted to a preset standard resolution range, and text records of different lengths are stored in a uniform encoding format. Finally, all the cleaned and normalized data is encapsulated into standard data objects containing basic data identification information. Each standard data object corresponds to a type or a specific piece of user content data, and the format is uniform.

[0022] Step 1.2: Based on a predefined feature attribute rule base, feature extraction is performed on each standard data object to generate a corresponding feature attribute set. The feature attribute set includes at least one attribute among data type, creation timestamp, data size, number of associated entities, and content quality score. Specifically, this includes: first, constructing a predefined feature attribute rule base. The feature attribute rule base sets corresponding feature extraction rules for different types of standard data objects in memory-type applications. For example, for text-type standard data objects that record the process of meeting, the rule base specifies that the data features to be extracted include data type, creation timestamp, data size, and number of associated entities; for image-type standard data objects in a shared album, the rule base specifies that the data features to be extracted include data type, creation timestamp, data size, and content quality score. Then, extraction rules matching each standard data object type are called from the rule base to extract features for each standard data object: for text-type standard data objects, the data type is determined to be text, the creation timestamp when the data was generated is extracted, the number of characters in the text is counted as the data size, and the number of users involved in the text is counted as the number of associated entities; for image-type standard data objects, the data type is determined to be image, the creation timestamp when the image was taken or uploaded is extracted, the storage size of the image file is read as the data size, and the image is scored according to the preset evaluation indicators such as clarity and color saturation in the rule base to obtain the content quality score. Finally, the features extracted from each standard data object are integrated to obtain the feature attribute set corresponding to the standard data object.

[0023] In this embodiment of the invention, the user's original content data is structured, cleaned, and normalized to unify the heterogeneous data format as the standard data object. Simultaneously, based on a predefined feature attribute rule base, each standard data object is extracted with a set of feature attributes including at least one of the following: data type, creation timestamp, data size, number of associated entities, and content quality score. This overcomes the shortcomings of existing technologies where heterogeneous user content data formats lead to a lack of unified data processing guidelines, and the lack of clear extraction of key data information affects the accuracy of data integrity judgment. This achieves the standardization of heterogeneous data, clarifies core data characteristics, and provides an accurate and consistent data source for subsequent data distribution analysis, evaluation parameter calculation, and rule engine judgment, ensuring the reliability of the entire intelligent content display method's data processing stage.

[0024] In a preferred embodiment of the present invention, step 2 above may include: Step 2.1: Based on the feature attribute set, determine one or more core analysis dimensions for data distribution analysis. Specifically, this includes: sorting out the feature attribute set of each data item in the memory application. The set includes attributes such as data type, creation timestamp, data volume, size, number of associated entities, content quality score, etc. Combining the core application requirements, namely, presenting the emotional journey in chronological order and distinguishing different types of memory content, key attributes are selected from the feature attribute set as core analysis dimensions. Considering that the time dimension can reflect the chronological order of memories, the data type dimension can distinguish different forms of memory content such as text, images, and graphics, and the data volume dimension can reflect the richness of a certain type of memory, these three attributes are directly related to the subsequent data distribution analysis and page display logic. Therefore, the creation timestamp data type and data volume are determined as the core analysis dimensions for data distribution analysis.

[0025] Step 2.2: Based on the determined core analysis dimensions, construct a multi-dimensional data distribution structure and map each data item to a coordinate point in the data distribution structure according to its feature attributes to obtain the mapped coordinate point set. Specifically, this includes: constructing a three-dimensional data distribution structure based on the determined three core analysis dimensions: creation timestamp, data type, and data volume. The three-dimensional data distribution structure is similar to a three-dimensional coordinate system, with each dimension corresponding to an axis of the coordinate system. A quantifiable scale is set for each dimension: the creation timestamp dimension is in days, with the scale range extending from the date the user first used the application to the current date; the data type dimension sets text to 1, images to 2, and mixed text and images to 3. The system uses numerical values ​​to correspond to different data types. The data size dimension is graded according to the data storage size: 0 to 100KB is set to 1, 101 to 500KB is set to 2, 501KB and above is set to 3. It iterates through each standard data object, extracts the numerical values ​​corresponding to the creation timestamp data type and data size from its feature attribute set, and uses these values ​​as coordinate values ​​to map them onto the three-dimensional data distribution structure to obtain a unique coordinate point. For example, a picture-type memory data stored on May 10, 2024, has a data storage size of 300KB, and its corresponding coordinate point is creation timestamp 20240510, data type 2, data size 2. After all data items are mapped, a complete set of coordinate points is obtained.

[0026] Step 2.3: Based on the mapped coordinate point set, and according to the preset data feature boundaries corresponding to each analysis dimension, divide the data distribution structure into multiple independent data representation intervals. Specifically, this includes: first, based on the user habits and data distribution characteristics of memory-type applications, preset the data feature boundaries corresponding to each core analysis dimension; creating a timestamp dimension with quarterly boundaries, with each interval being a 3-month unit, such as January 1, 2024 to March 31, 2024 as one boundary segment, and April 1, 2024 to June 30, 2024 as the next boundary segment; dividing the data type dimension with numerical boundaries, 1 to 1 for text, 2 to 2 for image, and 3 to 3 for mixed text and image; and dividing the data volume dimension with graded numerical boundaries, 1 to 1 for small volume, 2 to 2 for medium volume, and 3 to 3 for large volume. Based on the distribution of the mapped coordinate points in the three-dimensional data distribution structure and according to the preset boundary values, multiple independent three-dimensional regions are divided in the structure. Each region is a data representation interval. For example, creating a region with a timestamp in the second quarter of 2024, a data type of image, and a medium data size constitutes an independent data representation interval.

[0027] Step 2.4: Based on the divided data representation intervals, classify the coordinate points of each data item into the corresponding data representation interval according to their spatial location. Specifically, this includes: traversing all mapped coordinate points, extracting the specific values ​​of each coordinate point in the three dimensions of creation timestamp, data type, and data size, comparing these values ​​with the dimensional boundaries of each data representation interval to determine the spatial location of the coordinate point. If the creation timestamp value of a coordinate point falls within the boundary range of the second quarter of 2024, the data type value falls within the boundary range corresponding to the image, and the data size value falls within the boundary range corresponding to medium capacity, then the coordinate point is classified into the corresponding medium capacity data representation interval for the image category in the second quarter of 2024. Repeat this operation to classify all coordinate points into their respective independent data representation intervals according to their spatial location, ensuring that each coordinate point belongs to only one data representation interval, thus completing the interval classification of the data items.

[0028] In this embodiment of the invention, one or more core analysis dimensions for data distribution analysis are determined based on a set of feature attributes. A multi-dimensional data distribution structure is constructed based on these core analysis dimensions, and each data item is mapped to a coordinate point in the structure according to its feature attributes to form a set of coordinate points. According to the preset data feature boundaries corresponding to each analysis dimension, multiple independent data representation intervals are divided in the data distribution structure. Then, the coordinate points of each data item are classified into the corresponding data representation intervals according to their spatial location. This overcomes the shortcomings of existing technologies, such as the lack of clear dimensional guidance for the distribution analysis of user content data, the scattered and disordered state of data distribution, and the inability to accurately divide the data range, which leads to a lack of structured basis for data integrity assessment. This achieves a clear and orderly distribution of user content data in a multi-dimensional structure, providing a precise structured data foundation for quantifying the data aggregation state through centroid coordinates and calculating data integrity assessment parameters, thereby improving the scientificity and accuracy of data integrity analysis.

[0029] In a preferred embodiment of the present invention, step 3 above may include: Step 3.1: Based on each categorized data representation interval, extract the coordinate points corresponding to all data items within the interval to construct an independent coordinate point set for each interval. Specifically, this includes: organizing all the divided data representation intervals. These intervals need to cover user content data of different types, time ranges, and data volumes in memory-based applications. For example, they include medium-capacity data intervals for images in the second quarter of 2024, small-capacity data intervals for text in the first quarter of 2024, and large-capacity data intervals for mixed text and images in the second quarter of 2024. For each data representation interval, select all data items belonging to that interval one by one, and extract the coordinate points corresponding to these data items in the multi-dimensional data distribution structure. For example, in a medium-capacity data interval for images in the second quarter of 2024, there are 15 user-uploaded daily love photos. The coordinate points corresponding to these 15 photos need to be extracted. Organize all the coordinate points extracted in each interval separately to obtain an independent coordinate point set exclusive to the data representation interval, ensuring that each coordinate point set only contains the coordinates of the data items in the corresponding interval and is not confused with the coordinate points of other intervals.

[0030] Step 3.2: For each set of coordinate points constructed within a data representation interval, the centroid coordinates of the data representation interval point set are obtained by calculating the geometric center position. Specifically, this includes: for each independent set of coordinate points in each data representation interval, calculating the average value of the coordinates in each dimension according to the core analysis dimension corresponding to the data representation interval, and thus determining the geometric center position of the point set, i.e., the centroid coordinates. For the creation timestamp dimension, the timestamp of each coordinate point is first converted into a computable numerical form, and then the arithmetic mean of all values ​​is calculated to obtain the centroid coordinate value of the creation timestamp dimension; for the data type dimension, since each... The creation timestamp dimension value of each coordinate point is a fixed value. The arithmetic mean of the values ​​of all coordinate points in this dimension within the interval is directly calculated to obtain the centroid coordinate value of the data type dimension. For the data size dimension, the arithmetic mean of the values ​​of all coordinate points is calculated based on the creation timestamp dimension value to obtain the centroid coordinate value of the data size dimension. The average values ​​of the three dimensions are combined to obtain the centroid coordinate of the coordinate point set of each data representation interval. For example, after calculation, the centroid of the data in a certain interval is around June 1, 2024, the data type is mainly images, and the data size is mainly medium.

[0031] Step 3.3, based on the obtained centroid coordinates of each interval, and combined with the number of data items contained in each interval, quantifies the overall clustering state and distribution density of all data items in the data distribution structure. Specifically, this includes: firstly, analyzing the spatial distribution of the centroid coordinates of each data representation interval to quantify the overall clustering state: calculating the distance between the centroid coordinates of any two intervals in the three core dimensions; if the centroid coordinates of most intervals are concentrated in a certain time period in the creation timestamp dimension, a certain category in the data type dimension, and a certain level in the data size dimension, then the overall clustering state of all data items is determined to be highly concentrated; if the centroid coordinates of each interval are concentrated in the three core dimensions, then the overall clustering state of all data items is determined to be highly concentrated. If the data is distributed dispersedly across all dimensions without any obvious concentrated areas, the overall aggregation state is determined to be dispersed. Then, the distribution density is quantified: for each data representation interval, first determine the range of the data representation interval in each core dimension, calculate the product of the dimension range of the data representation interval, and then divide the number of data items contained in the data representation interval by the dimension range product to obtain the distribution density value of the data representation interval; take the average of the distribution density values ​​of all intervals, and combine it with the proportion of the number of data items in each interval to finally obtain the overall distribution density of all data items in the data distribution structure. If the overall density value is higher than the preset threshold, the distribution density is determined to be high, and vice versa.

[0032] Step 3.4: Based on the overall clustering state and distribution density, calculate the evaluation parameters used to comprehensively assess data integrity using preset weighted calculation rules. Specifically, these include: For the core functional requirements of memory-based applications, preset weighted calculation rules are established: weights are assigned based on the correlation between different data representation intervals and key application display pages. For example, a specific date segment related to the anniversary summary page, plus a mixed text and image data interval with a large data volume, has a weight of 0.3; any date segment related to the shared album page, plus images, plus a medium-to-large data volume interval has a weight of 0.25; and any date segment related to the regular chat history page, plus text, plus a small data volume interval has a weight of 0.1. Simultaneously, scoring standards are set for the overall clustering state and overall distribution density: highly concentrated clustering receives 5 points, moderately concentrated clustering receives 3 points, and dispersed clustering receives 1 point. High distribution density earns 5 points, medium earns 3 points, and low earns 1 point. Then, the evaluation parameters are calculated according to preset rules: First, the interval contribution value of each interval is calculated, which is the data representation interval weight multiplied by the data representation interval clustering score plus the interval distribution density score divided by 2; the interval contribution values ​​of all intervals are added together to obtain a sum, and then the sum is multiplied by the average of the overall clustering score and the overall distribution density score to obtain the final comprehensive evaluation parameter for data integrity. For example, if the sum of all interval contribution values ​​is 25, the overall clustering score is 4 points, the overall distribution density score is 4 points, and the average of the two is 4, then the evaluation parameter is 25 multiplied by 4 equals 100. If the sum of all interval contribution values ​​is 18, and the average of the overall clustering score and the overall distribution density score is 3, then the evaluation parameter is 18 multiplied by 3 equals 54.

[0033] Step 3.5 involves inputting the global content data and the calculated evaluation parameters into the predefined rule engine. This includes: first, collecting the user's global content data from memory-based applications. This data includes all the user's original data, such as text recordings of their meeting, daily romantic moments with pictures and text, anniversary dates and descriptions, shared album photos, and interactive chat snippets, as well as standard data objects that have undergone structured processing; then, extracting the comprehensive evaluation parameters calculated in Step 3.4, and transmitting the global content data and evaluation parameters together to the predefined rule engine. The rule engine has been pre-configured with judgment rules corresponding to the application's display pages, providing data support for the rule engine to traverse all predefined display pages and determine whether a page meets the display conditions.

[0034] In this embodiment of the invention, the coordinate points corresponding to all data items within each data representation interval after classification are extracted to construct an independent set of coordinate points for each interval. The geometric center position of each interval point set is calculated to solve the centroid coordinates. The overall aggregation state and distribution density of all data items in the data distribution structure are quantified by combining the number of data items contained in each interval. The evaluation parameters for comprehensively evaluating data integrity are calculated according to the preset weighted calculation rules. The global content data and the evaluation parameters are input into the predefined rule engine. This overcomes the shortcomings of the prior art, which only qualitatively judges whether the data exists to evaluate data integrity, lacks quantitative analysis of the degree of data aggregation and distribution, and leads to one-sided and inaccurate data integrity evaluation, thus affecting the rationality of the display page selection. This achieves accurate quantitative evaluation of data integrity, provides a comprehensive and objective basis for the rule engine to judge whether the display page meets the integrity conditions, ensures that the subsequently generated target page sequence is more in line with the actual data richness and distribution characteristics of the user, and improves the scientificity and accuracy of content display decisions.

[0035] In a preferred embodiment of the present invention, step 4 above may include: Step 4.1: Based on the input global content data and the evaluation parameters, initialize the rule engine and load all predefined display pages and corresponding data integrity conditions. Specifically, this includes: acquiring the user's global content data from the memory-based application, including records of the user's meeting time, text and image data of daily dating activities, number of anniversary markers, number of pictures in shared albums, etc.; simultaneously extracting the calculated comprehensive evaluation parameters for data integrity, for example, an evaluation parameter score of 52 points; based on this global content data and evaluation parameters, starting and initializing the rule engine to ensure that the rule engine can read and call this data for subsequent judgments; and loading all predefined display pages from the application's configuration library. These pages include... This includes pages for meeting history, daily dating, anniversaries, and shared albums. It also loads data integrity conditions for each page. For example, the data integrity condition for the meeting history page is that there is a record of the meeting time in the global content data and the evaluation parameter is not less than 40 points; the data integrity condition for the daily dating page is that there are not less than 2 pieces of daily dating image and text data in the global content data and the evaluation parameter is not less than 35 points; the data integrity condition for the anniversaries summary page is that there are not less than 3 anniversaries marked in the global content data and the evaluation parameter is not less than 45 points; and the data integrity condition for the shared album page is that there are not less than 5 shared album photos in the global content data and the evaluation parameter is not less than 40 points.

[0036] Step 4.2: Based on the loaded set of display pages, the rule engine iterates through each display page sequentially. Specifically, this includes: organizing the loaded set of display pages in the rule engine and determining the traversal order, for example, traversing in the order of the acquaintance record page, the daily love life page, the anniversary summary page, and the shared photo album page. The rule engine selects each display page in the set in this order and performs data integrity condition matching judgment on each page individually to ensure that each predefined display page is processed one by one without missing any page.

[0037] Step 4.3: During the traversal process, for the currently traversed display page, based on the global content data and evaluation parameters, a matching judgment is performed on the corresponding data integrity conditions to obtain the matching judgment result. Specifically, when the rule engine traverses to the currently displayed page, it first retrieves the preset data integrity conditions corresponding to the page, and then combines the input global content data and evaluation parameters to perform a matching judgment. For example, when traversing to the acquaintance record page, it first checks whether there is an acquaintance time record in the global content data. After checking, it finds that there is an acquaintance time record for January 15, 2024. Then it checks whether the evaluation parameter score of 52 is not lower than the condition requirement of 40. Both are satisfied, so it is determined that the acquaintance record page meets the data integrity conditions, and a satisfactory matching judgment result is obtained. When traversing to the anniversary summary page, it checks that the number of anniversary markers in the global content data is 2, which is lower than the condition requirement of 3. Even if the evaluation parameter score of 52 is not lower than 45, it is still determined that the anniversary summary page does not meet the data integrity conditions, and a unsatisfactory matching judgment result is obtained. Following the same method, the matching and judgment were performed on the daily dating page and the shared photo album page in turn, and the corresponding results of satisfaction or dissatisfaction were obtained respectively.

[0038] Step 4.4: Based on the matching judgment results, record whether the traversed display pages meet the display conditions, and update the mapping relationship judgment result set. Specifically, after obtaining the matching judgment result of the current display page, immediately record the name of the page and the corresponding judgment result. For example, if the acquaintance record page meets the conditions, record the acquaintance record page and the condition of meeting the display conditions; if the anniversary summary page does not meet the conditions, record the anniversary summary page - not meeting the display conditions. At the same time, add these records to the mapping relationship judgment result set. Update the set once after each page judgment and recording is completed to ensure that the set contains the names of the traversed pages and their corresponding display condition satisfaction status in real time, avoiding the problem of record omission or update delay.

[0039] Step 4.5: After completing the traversal and judgment of all display pages, output the final mapping relationship judgment result. Specifically, after the rule engine completes the traversal and judgment of all predefined display pages, it performs a final organization of the mapping relationship judgment result set to ensure that the set contains the names of all display pages and their corresponding judgment results. For example, the set content is: Acquaintance record page: satisfied, daily love page: satisfied, anniversary summary page: not satisfied, shared album page: satisfied. This organized mapping relationship judgment result set is output to the application's processing module. The processing module transforms the rule engine's judgment result into a displayable page sequence by filtering valid pages and dynamically arranging the order, ensuring a display effect without blank pages. The sorting optimization improves the continuity of content display and user experience, which is the core execution link for realizing intelligent display based on data integrity. It provides a basis for dynamically generating the target page sequence so that the module can filter out the pages to be displayed based on the satisfied judgment results and remove the unsatisfied pages to avoid blank pages.

[0040] In this embodiment of the invention, the rule engine is initialized based on input global content data and evaluation parameters, and all predefined display pages and their corresponding data integrity conditions are loaded. The rule engine sequentially traverses each loaded display page, and during the traversal, it combines global content data and evaluation parameters to match and judge the corresponding data integrity conditions of the current page. Based on the judgment result, it records whether the page meets the display conditions and updates the mapping relationship judgment result set. After completing the traversal of all pages, the final mapping relationship judgment result is output. This overcomes the shortcomings of the prior art, where the rule engine startup lacks global data and quantitative evaluation parameter support, and the page integrity judgment is not systematically traversed and lacks a standardized recording mechanism. This results in one-sided page selection criteria, chaotic judgment results, and an inability to accurately identify pages that meet the display conditions. This invention ensures that the rule engine judgment has comprehensive and accurate data support, the page traversal judgment process is standardized and orderly, and the judgment results are traceable. It ensures that the final output mapping relationship judgment result is accurate and reliable, providing a solid basis for dynamically generating a target page sequence without blank pages.

[0041] In a preferred embodiment of the present invention, step 5 above may include: Step 5.1: Read the mapping relationship judgment results and identify all display pages that meet the data integrity conditions to form a candidate page set. Specifically, this includes: First, obtaining the mapping relationship judgment results output by the rule engine. This result contains the names of all predefined display pages in the memory-type application and their corresponding data integrity satisfaction status. For example, it includes information such as the "acquaintance record page" (satisfied), "relationship daily life page" (satisfied), "anniversary summary page" (not satisfied), "shared album page" (satisfied), and "user interaction record page" (not satisfied). Next, each display page in the mapping relationship judgment results is identified one by one. Pages marked as meeting the data integrity conditions are filtered out, while pages marked as not meeting the conditions are removed. For example, the "acquaintance record page," "relationship daily life page," and "shared album page" are identified as meeting the conditions, while the "anniversary summary page" and "user interaction record page" are not. The three pages that meet the conditions are collected and organized to obtain a candidate page set, ensuring that the candidate page set only contains display pages with complete data support and no placeholder prompts are displayed.

[0042] Step 5.2: Based on the evaluation parameters and preset page priority rules, dynamically sort the displayed pages in the candidate page set. This includes: first, retrieving the calculated comprehensive evaluation data integrity parameters. These parameters reflect the aggregation state and distribution density of the data corresponding to each candidate page. For example, the shared album page has the highest evaluation parameter score due to its large amount of image data and high distribution density; the daily dating page has a medium evaluation parameter score due to its moderate amount of text and image data and a high proportion of recent data; and the acquaintance record page has a low evaluation parameter score because it only contains a small amount of early text records. Simultaneously, the preset page priority rules for memory-type applications are retrieved. These rules are set based on the core needs of the application, specifically prioritizing large amounts of data. Furthermore, pages with high user access frequency are prioritized over pages with less data, pages with recently generated data are prioritized over pages with earlier data, and pages that can intuitively present emotional details are prioritized over pages containing only basic information. Subsequently, the candidate page set is dynamically sorted based on evaluation parameters and priority rules: based on the initial ranking of evaluation parameter scores, the shared album page has the highest score and is temporarily ranked first, the daily dating page is second, and the acquaintance record page has the lowest score and is temporarily ranked third; then, based on the priority rules, it is verified and adjusted that although the daily dating page has a slightly lower score than the shared album page, it has a higher proportion of recent data and users visit it more often per week than the acquaintance record page, so there is no need to adjust the order. The final ranking result is the shared album page, daily dating page, and acquaintance record page.

[0043] Step 5.3: Based on the dynamic sorting results, the candidate pages are combined sequentially to obtain the final target page sequence. Specifically, the candidate pages are arranged and combined in order of their sorting based on the obtained dynamic sorting results to form the final target page sequence. Specifically, the first-ranked common album page is used as the first page of the target page sequence, the second-ranked daily life dating page is used as the second page of the sequence, and the third-ranked meeting record page is used as the third page of the sequence. This combination forms the target page sequence of common album page, daily life dating page, and meeting record page. This sequence ensures that each page has complete data support, with no blank spaces, and the page order matches the user's focus on the content of the memories—from data-rich picture memories to recent daily emotional records, and then to early basic information about meeting. This meets the core requirement of memory-based applications to present the user's emotional journey in a coherent manner, providing a clear and orderly basis for page loading and rendering.

[0044] In this embodiment of the invention, because the invention uses the result of reading the mapping relationship to identify all display pages that meet the data integrity conditions and form a candidate page set, the display pages in the candidate page set are dynamically sorted based on the evaluation parameters and preset page priority rules. Then, the candidate pages are combined in order according to the dynamic sorting result to obtain the final target page sequence. This overcomes the problem in the prior art where the fixed page structure leads to blank pages without data and the page display order cannot match the user's data characteristics and actual needs, causing users to repeatedly skip meaningless pages and resulting in a lack of personalized browsing experience. This invention completely eliminates blank pages, ensures that all candidate pages have complete data support, and makes the display order of the target page sequence conform to the user's data richness and usage preferences, improving the coherence and personalization of content display and optimizing the user's immersive browsing experience.

[0045] In a preferred embodiment of the present invention, step 6 above may include: Step 6.1: Receive the target page sequence, obtain page configuration information by parsing the target page sequence, and initialize the page rendering environment based on the parsed configuration information. Specifically, this includes: receiving the target page sequence, which consists of valid pages filtered and sorted for memory-related applications, such as pages for shared photo albums, daily dating moments, and meeting records. Next, parse the target page sequence to extract configuration information for each page. This configuration information includes page type, storage path of resources within the page, page layout style, and interaction response rules. Based on this parsed configuration information, initialize the page rendering environment: first, adapt to the screen size and resolution of the user's device to ensure the page display ratio meets device requirements; then, load the basic components required for rendering, such as image rendering modules, text rendering modules, and interaction response modules, and simultaneously set rendering parameters to prepare for the subsequent loading and rendering of page content.

[0046] Step 6.2: Based on the initialized rendering environment, a lazy loading strategy is adopted to load and render the content of the pages currently located in the visible area of ​​the target page sequence. Specifically, this includes: In the initialized rendering environment, a lazy loading strategy is used to process page content: First, the pages currently located in the visible area of ​​the user's device in the target page sequence are identified. For example, after the application starts, the first page of the target page sequence, the shared album page, is displayed by default, and this page is within the visible area. Only the content resources of the page in this visible area are loaded, specifically including fetching all image resources and corresponding text descriptions of the shared album page from the storage path corresponding to the configuration information. Subsequent pages such as the daily love life page and the meeting record page in the sequence are not loaded. After loading, the images are arranged and displayed according to a preset layout through the image rendering module in the rendering environment, and the text descriptions are matched and placed below each image through the text rendering module, completing the rendering of the shared album page and avoiding the initial slow loading and resource waste caused by loading all page content at once.

[0047] Step 6.3: After the initial page rendering is completed, the swipe switching operation in the page sequence triggers the preloading request of adjacent pages based on the swipe direction and distance. Specifically, this includes: after the initial page rendering is completed, real-time monitoring of the user's swipe switching operation in the page sequence: when the user's finger swipes upward on the screen, the swipe direction is identified as upward, and the swipe distance is calculated through the device sensors. If the swipe distance exceeds a preset threshold, it is determined that the user intends to switch to the next page, triggering the preloading request of the next page of the common album page in the target page sequence, namely the Love Daily page; if the user's finger swipes downward on the screen, and the current page is not the first page in the sequence, and the swipe distance exceeds the preset threshold, the preloading request of the previous page is triggered, ensuring that the user does not need to wait for content to load when switching pages.

[0048] Step 6.4: Based on the preload request, dynamically load the content resources of the adjacent pages. Simultaneously, according to changes in the currently active pages, perform resource reclamation operations on pages that have moved out of the visible area. Specifically, upon receiving a preload request from an adjacent page, dynamically load the page's content resources based on the page configuration information corresponding to the request. For example, upon receiving a preload request for the "Love Daily Life" page, retrieve the page's image and text resources from the configured storage path and complete preliminary resource processing in the background, but do not render or display them immediately. Simultaneously, monitor changes in the currently active pages. When the original active page completely moves out of the user's visible area, perform resource reclamation operations on the shared album page that has moved out of the visible area: release the memory resources occupied by the page, close the background processes associated with the page, avoid invalid page resources occupying device memory for extended periods, and optimize device performance.

[0049] Step 6.5: Based on the current rendering state and page switching action, automatically trigger the associated transition animation effects to complete the intelligent content display of user data integrity. Specifically, this includes: real-time monitoring of the current page rendering state and the user's page switching action, and automatically triggering associated transition animation effects based on the matching relationship between the two. For example, when the user swipes up from the shared album page to switch to the daily life of the relationship page, and the daily life of the relationship page has been preloaded, a swipe transition animation is triggered, causing the shared album page to gradually disappear towards the bottom of the screen as the user swipes, while the daily life of the relationship page gradually enters from the top of the screen. The animation duration is set to 0.3 seconds to ensure a smooth and natural switching process. If the user swipes down from the daily life of the relationship page back to the shared album page, a reverse swipe transition animation is triggered. When all page switching and rendering are complete, the user can smoothly browse all content in the target page sequence without blank pages or lag. This ultimately completes the intelligent content display of the memory-based application based on user data integrity, achieving a coherent presentation of the emotional journey.

[0050] In this embodiment of the invention, after receiving the target page sequence, its page configuration information is parsed to initialize the page rendering environment. Based on the initialized page rendering environment, a lazy loading strategy is used to load and render only the page content of the currently visible area. After the initial rendering is completed, a preloading request for adjacent pages is triggered according to the direction and distance of the page swiping operation. The content resources of adjacent pages are dynamically loaded according to the preloading request, and resource reclamation operations are performed on pages that have moved out of the visible area. At the same time, the associated transition animation effect is automatically triggered in combination with the current rendering state and page switching action. This overcomes the problems of the prior art, such as wasting resources due to full loading of all predefined page frames, slow initial loading speed, users having to repeatedly skip meaningless blank pages which disrupts the continuity of content, and the lack of smooth transition effects when switching pages, which leads to a lack of immersive browsing experience. This invention improves the initial loading speed of the page, reduces the use of device resources, completely eliminates blank pages to ensure the continuity of content display, and optimizes the smoothness of page switching through transition animations, creating an immersive intelligent content display experience for users that meets the needs of emotional journey presentation in memory-type applications.

[0051] like Figure 2 As shown, embodiments of the present invention also provide an intelligent content display system based on data integrity, including: The acquisition module is used to acquire global content data, perform structured processing on multiple data items contained in the global content data, and represent each data item as a set of feature attributes; The evaluation module performs data distribution analysis based on a set of feature attributes to construct a structured data distribution structure. Within this structure, multiple distinct data representation intervals are defined according to preset data feature boundaries. Each data item is then categorized into its corresponding interval, resulting in a categorized data representation interval. A point set is constructed based on the data items within each interval. By calculating the centroid coordinates of the point set within each interval, the aggregation state of the data items is quantified using these coordinates, and an evaluation parameter is calculated to comprehensively assess data integrity. The global content data and the evaluation parameter are then input into a predefined rule engine. The processing module is used to traverse all predefined display pages based on the rule engine and global content data, determine whether each display page meets the corresponding data integrity conditions according to the mapping relationship, and obtain the mapping relationship judgment result; based on the mapping relationship judgment result, dynamically generate a target page sequence of display pages that meet the data integrity conditions; based on the target page sequence, load and render the target page sequence to complete the intelligent content display of user data integrity.

[0052] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent content display based on data integrity, characterized in that, The method includes: Obtain global content data, perform structured processing on multiple data items contained in the global content data, and represent each data item as a set of feature attributes; Based on the set of feature attributes, data distribution analysis is performed to construct a structured data distribution structure. In the data distribution structure, multiple different data representation intervals are divided according to the preset data feature boundaries, and each data item is classified into the corresponding data representation interval to obtain each classified data representation interval. Based on the data items within each data representation interval after classification, a point set is constructed. By solving the centroid coordinates of the point set within each interval, the aggregation state of the data items is quantified using the centroid coordinates of the point set. An evaluation parameter for comprehensively evaluating data integrity is calculated. The global content data and the evaluation parameter are then input into the predefined rule engine. Based on the rule engine and global content data, all predefined display pages are traversed, and each display page is judged to meet the corresponding data integrity conditions according to the mapping relationship in order to obtain the mapping relationship judgment result. Based on the mapping relationship, a target page sequence that satisfies the data integrity condition is dynamically generated. Based on the target page sequence, intelligent content display is achieved by loading and rendering the target page sequence to ensure the integrity of user data.

2. The intelligent content display method based on data integrity according to claim 1, characterized in that, Retrieve global content data, perform structured processing on the multiple data items contained within the global content data, and represent each data item as a set of feature attributes, including: Obtain the user's original content data, perform structured cleaning and normalization on the original content data, and unify the heterogeneous data format into a standard data object; Based on a predefined feature attribute rule base, features are extracted for each standard data object to generate a corresponding feature attribute set; the feature attribute set includes at least one attribute among data type, creation timestamp, data size, number of associated entities, and content quality score.

3. The intelligent content display method based on data integrity according to claim 2, characterized in that, Based on a set of feature attributes, data distribution analysis is performed to construct a structured data distribution structure. Within this structure, multiple distinct data representation intervals are defined according to predefined data feature boundaries. Each data item is then categorized into its corresponding data representation interval, resulting in each categorized data representation interval, including: Based on the set of feature attributes, determine one or more core analytical dimensions for data distribution analysis; Based on the determined core analysis dimensions, a multi-dimensional data distribution structure is constructed, and each data item is mapped to a coordinate point in the data distribution structure according to its feature attributes, so as to obtain the mapped coordinate point set. Based on the mapped set of coordinate points, and according to the preset data feature boundaries corresponding to each analysis dimension, multiple independent data representation intervals are divided in the data distribution structure. Based on the predefined data representation intervals, the coordinates of each data item are categorized into the corresponding data representation interval according to their spatial location.

4. The intelligent content display method based on data integrity according to claim 3, characterized in that, Based on the data items within each data representation interval after classification, a point set is constructed. By solving the centroid coordinates of the point set within each interval, the aggregation state of the data items is quantified using the centroid coordinates of the point set, and an evaluation parameter for comprehensively evaluating data integrity is calculated. The global content data and evaluation parameters are input into a predefined rule engine, including: Based on each data representation interval after classification, the coordinate points corresponding to all data items within the interval are extracted to construct an independent set of coordinate points for each interval; For each set of coordinate points constructed within a data representation interval, the centroid coordinates of the data representation interval point set are obtained by calculating the geometric center position. Based on the centroid coordinates of each interval obtained by the solution, and combined with the number of data items contained in each interval, the overall clustering state and distribution density of all data items in the data distribution structure are quantified. Based on the overall aggregation state and distribution density, evaluation parameters for comprehensively assessing data integrity are calculated using preset weighted calculation rules. The global content data and the calculated evaluation parameters are input into the predefined rule engine.

5. The intelligent content display method based on data integrity according to claim 4, characterized in that, Based on the rule engine and global content data, all predefined display pages are traversed. Each display page is then judged to meet the corresponding data integrity conditions according to the mapping relationship, thus obtaining the mapping relationship judgment result; including: Based on the input global content data and the evaluation parameters, the rule engine is initialized, and all predefined display pages and corresponding data integrity conditions are loaded. Based on the already loaded collection of display pages, each display page is traversed sequentially using the rule engine; During the traversal, for the currently traversed display page, based on the global content data and evaluation parameters, a matching judgment is made on the corresponding data integrity conditions to obtain the matching judgment result; Based on the matching results, record whether the traversed display pages meet the display conditions, and update the mapping relationship judgment result set; After traversing and judging all displayed pages, output the final mapping relationship judgment result.

6. The intelligent content display method based on data integrity according to claim 5, characterized in that, Based on the mapping relationship, a target page sequence that satisfies the data integrity requirements is dynamically generated, including: Read the mapping relationship judgment results, identify all display pages that meet the data integrity conditions, and form a candidate page set; Based on evaluation parameters and preset page priority rules, the display pages in the candidate page set are dynamically sorted. Based on the dynamic sorting results, the candidate pages are combined in order to obtain the final target page sequence.

7. The intelligent content display method based on data integrity according to claim 6, characterized in that, Based on the target page sequence, intelligent content display is achieved by loading and rendering the target page sequence to ensure the integrity of user data, including: Receive the target page sequence, obtain page configuration information by parsing the target page sequence, and initialize the page rendering environment based on the parsed configuration information; Based on the initialized rendering environment, a lazy loading strategy is adopted to load and render the page content currently located in the visible area of ​​the target page sequence. After the initial page rendering is completed, the swipe switching operation in the page sequence triggers the preloading request of adjacent pages based on the swipe direction and distance; Based on the preloading request, the content resources of the adjacent pages are dynamically loaded. At the same time, according to the changes of the currently active pages, resource reclamation operations are performed on the pages that have been moved out of the visible area. Based on the current rendering state and page switching actions, the associated transition animation effects are automatically triggered to complete the intelligent content display that ensures the integrity of user data.

8. An intelligent content display system based on data integrity, wherein the system implements the method as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire global content data, perform structured processing on multiple data items contained in the global content data, and represent each data item as a set of feature attributes; The evaluation module is used to perform data distribution analysis based on the set of feature attributes to construct a structured data distribution structure. In the data distribution structure, multiple different data representation intervals are divided according to the preset data feature boundaries, and each data item is classified into the corresponding data representation interval to obtain each classified data representation interval. Based on the data items within each data representation interval after classification, a point set is constructed. By solving the centroid coordinates of the point set within each interval, the aggregation state of the data items is quantified using the centroid coordinates of the point set. An evaluation parameter for comprehensively evaluating data integrity is calculated. The global content data and the evaluation parameter are then input into the predefined rule engine. The processing module is used to traverse all predefined display pages based on the rule engine and global content data, determine whether each display page meets the corresponding data integrity conditions according to the mapping relationship, and obtain the mapping relationship judgment result; based on the mapping relationship judgment result, dynamically generate a target page sequence of display pages that meet the data integrity conditions; based on the target page sequence, load and render the target page sequence to complete the intelligent content display of user data integrity.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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