A matrix data layering intelligent management system

Abstract

This invention provides a matrix-based data hierarchical intelligent management system, belonging to the field of intelligent social media marketing technology. The system includes a multi-source data acquisition module, a data matrix construction module, a hierarchical processing and scheduling module, an intelligent profile generation module, and a multi-version content generation module. The multi-source data acquisition module acquires historical user interaction data from multiple platforms, preprocesses it, and outputs standardized raw data. The data matrix construction module classifies the standardized data, scores the value of data units, matches levels, and constructs a data matrix based on attributes. The hierarchical processing and scheduling module selects a basic strategy based on the matrix, adjusts it according to value levels, and outputs processed data. The intelligent profile generation module generates user profiles based on the processed data. The multi-version content generation module filters high-value data and combines it with profiles to generate multi-version adapted content. This system can improve data management efficiency, profile accuracy, and content adaptability.

Description

Technical Field

[0001] This invention relates to the field of intelligent social media marketing technology, specifically to a matrix-style data hierarchical intelligent management system. Background Technology

[0002] With the deepening of digital operations, enterprises generally acquire users' historical interaction data through multiple platforms, hoping to achieve core operational goals such as user profiling and precise content delivery through the management, analysis, and application of this data, thereby improving user stickiness and conversion efficiency. Currently, the industry has formed a technical chain of data collection, processing, profiling, and content generation. However, key bottlenecks still exist in crucial areas such as handling heterogeneous data across multiple platforms, matching data value with resources, and synergizing profiling accuracy with content adaptability.

[0003] However, existing technical solutions have two major flaws: First, there is a lack of a unified value assessment system for heterogeneous data across multiple platforms, resulting in the mixed processing of high- and low-value data. Furthermore, data processing employs a single strategy and fails to allocate resources according to the differences in data value, leading to insufficient extraction of core features from high-value data and the consumption of redundant computing power by low-value data, resulting in low processing efficiency. Second, user profiling relies on the average extraction of features from the entire dataset, which is not accurate enough. Subsequent content generation across multiple platforms simply applies fixed templates without combining core user characteristics with platform specifications, resulting in poor adaptability of content to user needs and platform characteristics. Summary of the Invention

[0004] This invention provides a matrix-style data hierarchical intelligent management system to solve problems such as low processing efficiency and low accuracy of user profiles leading to poor adaptability of generated content in existing technologies.

[0005] To achieve the above objectives, embodiments of the present invention provide a matrix-based data hierarchical intelligent management system. This system includes: a multi-source data acquisition module, comprising a cross-platform data interface and a preprocessing unit; the cross-platform data interface acquiring historical user interaction data across multiple platforms by connecting to the official data APIs of each platform; the preprocessing unit performing format unification and anomaly cleaning on the historical interaction data, and outputting standardized raw data; and a data matrix construction module, used to classify the standardized raw data, assign a value score to each data unit in each category of raw data, and match corresponding value levels based on the value scores of each data unit. The system constructs a data matrix by combining the attributes of each data unit; a hierarchical processing and scheduling module is used to select the basic processing strategy corresponding to various types of raw data based on the data matrix, and also to differentiate the basic processing strategy according to the value level of the data unit, prioritizing the allocation of processing resources to high-value data units and outputting the processed data; an intelligent profile generation module is used to extract profile features from the processed data in a hierarchical manner, and generate user profiles in combination with the characteristics of the target platform; a multi-version content generation module is used to select a set of high-value data units that match the characteristics of the target platform based on the value level and data category of the data units recorded in the data matrix, and generate multi-version adapted content in combination with the user profiles.

[0006] Optionally, the matrix-style data hierarchical intelligent management system pre-stores a business target label library and an evaluation dimension library. The business target label library contains core business targets and quantitative indicators corresponding to different data categories, and the evaluation dimension library contains evaluation dimensions corresponding to different data categories.

[0007] Optionally, the value scoring process includes: after the raw data is classified, for each type of raw data, the data matrix construction module calls the evaluation dimension corresponding to that type of raw data from the evaluation dimension library, and calls the core business objectives and quantitative indicators corresponding to that type of data from the business objective label library; through a differentiated feature extraction algorithm, real-time features related to the evaluation dimensions are extracted from each type of raw data; based on a preset mapping rule, the matching degree between the real-time features and the corresponding quantitative indicators is calculated to obtain the original matching value of each evaluation dimension, and the original matching value is converted into a real-time score through a preset standardization algorithm; according to a preset business weight table, the real-time scores of each evaluation dimension are weighted and summed to obtain the value score of each data unit in that type of raw data.

[0008] Optionally, the value level includes three levels: high, medium, and low, and each value level corresponds to a value scoring range. The data matrix construction module determines the corresponding value level by matching the value score of each data unit with the value scoring range.

[0009] Optionally, the selection and differentiated adjustment of the basic processing strategy includes: matching exclusive basic strategies for each type of raw data based on the core characteristics of each type of raw data in the data matrix; adding enhancement mechanisms to the basic strategy to strengthen feature extraction accuracy and processing priority for high-value data units; and reducing processing steps and compressing resource consumption ratio in the basic strategy for medium and low-value data units.

[0010] Optionally, the layered extraction process includes: for the processed data, extracting deep behavioral features from high-value data units and basic attribute features from medium- and low-value data units; based on the data category information recorded in the data matrix, performing aggregation analysis on the two features of data units under the same data category to form a multi-dimensional feature subset; combining the characteristics of the target platform, assigning weights to the feature subsets of each dimension, and integrating the weighted feature subsets through a feature fusion algorithm to generate a user profile.

[0011] Optionally, the screening process for the high-value data unit set includes: extracting high-value data units from all data units; extracting user features associated with the high-value data units based on the generated user profile; calculating the matching degree with the target platform characteristics based on the extracted data units and the attribute information corresponding to each data unit, and selecting data units with a matching degree higher than a preset threshold as high-value data units; verifying the processing precision identifier of the high-value data units based on the processed data output by the hierarchical processing and scheduling module, and removing data units that fail the precision verification; and selecting other high-value data units of other categories that are strongly associated with the high-value data units based on the category association relationships recorded in the data matrix through a cross-category association algorithm, thus forming the final high-value data unit set.

[0012] Optionally, generating multi-version adapted content includes: extracting feature subsets and their corresponding weights from the user profiles of the intelligent profile generation module that correspond to the category to which the high-value data unit set belongs; dividing multiple adaptation scenarios according to the content format specifications of the target platform, and matching exclusive content generation templates for each adaptation scenario; and filling the high-value data unit set into the corresponding template according to preset rules for each adaptation scenario to generate multi-version adapted content.

[0013] Optionally, a feedback optimization module is also included, which is used to collect user feedback data on the adapted content of each version, select the optimal generation logic based on the feedback results, and guide the generation of subsequent adapted content.

[0014] This invention provides a matrix-style hierarchical intelligent data management system. Through a multi-source data acquisition module, it achieves format unification and anomaly cleansing of historical interactive data from multiple platforms, providing a standardized data foundation for subsequent processing. A data matrix construction module categorizes data and performs value scoring and grading matching to clarify data value differences. A hierarchical processing and scheduling module allocates processing resources according to data value grades, ensuring the accuracy of in-depth processing of high-value data units while avoiding redundant resource consumption by low- and medium-value data units, thus improving overall data processing efficiency. An intelligent profile generation module extracts features from high-, medium-, and low-value data units and generates profiles based on target platform characteristics, improving the accuracy of user profiles. A multi-version content generation module then selects a set of high-value data units that match the characteristics of the target platform and generates suitable content based on accurate user profiles. Ultimately, this achieves efficient end-to-end management of multi-platform data from acquisition and processing to profile construction and content generation, effectively solving problems such as difficulty in identifying data value across multiple platforms, misallocation of processing resources, coarse user profiles, and poor content adaptability. This maximizes data value utilization efficiency and improves the accuracy and effectiveness of data-driven operations. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0016] Figure 1 This is a framework diagram of the matrix-style data hierarchical intelligent management system provided in an embodiment of the present invention;

[0017] Figure 2 This is a flowchart of the data unit value scoring process provided in an embodiment of the present invention;

[0018] Figure 3 This is a flowchart of the high-value data unit screening process provided in an embodiment of the present invention;

[0019] Figure 4 This is a flowchart of the multi-version content generation process provided in the embodiments of the present invention. Detailed Implementation

[0020] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0021] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0022] With the deepening of multi-platform user data-driven operations, existing data management systems suffer from several problems: difficulty in identifying the value of heterogeneous data across platforms leads to insufficient mining of high-value information; processing resources are not allocated according to data value differentiation, resulting in wasted computing power; user profiles rely on the average extraction of features from all data, leading to insufficient accuracy; and content generation uses only fixed templates, resulting in poor platform adaptability. Therefore, it is particularly urgent to develop a matrix-style hierarchical intelligent data management solution and system that can deeply integrate data value assessment, hierarchical resource scheduling, accurate profile construction, and multi-version content adaptation.

[0023] To address this issue, this invention provides a matrix-style data hierarchical intelligent management system. This system unifies cross-platform data formats and cleans and outputs data through a multi-source data acquisition module; a data matrix construction module clarifies data value through classification scoring and value level matching; a hierarchical processing module optimizes resource allocation based on value; an intelligent profiling module extracts features hierarchically to generate precise profiles; a multi-version content module combines the profiles to generate adapted content; and a feedback optimization module forms a closed loop, achieving a synergistic improvement in data value utilization, resource allocation rationality, and operational accuracy.

[0024] The following is combined with Figures 1-4 This invention is described in detail.

[0025] like Figure 1As shown, this embodiment of the invention provides a matrix-style data hierarchical intelligent management system. The matrix-style data hierarchical intelligent management system includes: a multi-source data acquisition module, comprising a cross-platform data interface and a preprocessing unit; the cross-platform data interface acquires historical user interaction data across multiple platforms by connecting to the official data APIs of each platform; the preprocessing unit performs format unification and anomaly cleaning on the historical interaction data and outputs standardized raw data; and a data matrix construction module, used to classify the standardized raw data, assign a value score to each data unit in each category of raw data, and match corresponding value levels based on the value scores of each data unit, and combine these with various... The system comprises several modules: a data matrix constructed from the attributes of data units; a hierarchical processing and scheduling module, used to select basic processing strategies corresponding to various types of raw data based on the data matrix, and to differentiate the basic processing strategies according to the value level of data units, prioritizing the allocation of processing resources to high-value data units and outputting the processed data; an intelligent profile generation module, used to extract profile features from the processed data and generate user profiles in combination with the characteristics of the target platform; and a multi-version content generation module, used to filter a set of high-value data units that match the characteristics of the target platform based on the value level and data category of the data units recorded in the data matrix, and to generate multi-version adapted content in combination with the user profiles.

[0026] The cross-platform data interface establishes authorized connections with official data APIs of various platforms, such as overseas social media and cross-border e-commerce, to achieve targeted acquisition of historical user interaction data from multiple platforms. This historical interaction data specifically covers users' active behavior data, passive behavior data, and basic interaction data on each platform. Format unification converts unstructured and semi-structured data returned from different platforms into a system-preset structured data format. Anomaly cleaning removes low-value interference data. Data classification divides standardized raw data according to the attributes of the data source platform and the data business value dimension. For example, by platform attributes, it is divided into social media interaction data, e-commerce behavior data, and basic user data; by business value dimension, it is divided into conversion-related data, interaction depth data, and basic reach data. The classification results ensure the relevance of data units to subsequent business scenarios.

[0027] Specifically, the matrix-style data hierarchical intelligent management system provided in this embodiment of the invention first obtains users' historical interaction data across multiple platforms by connecting to the official APIs of various platforms through the cross-platform data interface of the multi-source data acquisition module. The preprocessing unit performs format unification and anomaly cleaning, outputting standardized raw data. Then, the data matrix construction module classifies the standardized data, scores the value of data units, and matches value levels, constructing a data matrix based on attributes. Subsequently, the hierarchical processing and scheduling module selects basic processing strategies based on the data matrix, adjusts them according to value levels, and outputs the processed data. The intelligent profile generation module extracts features hierarchically and generates user profiles based on the characteristics of the target platform. Finally, the multi-version content generation module filters high-value data sets that match the target platform and generates multi-version adapted content based on the profiles. This process achieves standardized integration and value explicitness of heterogeneous data from multiple platforms, improves processing efficiency through value-driven resource allocation, and generates accurate profiles and adapted content based on hierarchical feature extraction and platform adaptation. It effectively solves the problems of difficult data value identification, resource mismatch, coarse profiles, and poor content adaptability, significantly improving data value utilization, rationality of processing resources, and operational accuracy.

[0028] Preferably, the matrix-style data hierarchical intelligent management system pre-stores a business target tag library and an evaluation dimension library. The business target tag library contains core business targets and quantitative indicators corresponding to different data categories, and the evaluation dimension library contains evaluation dimensions corresponding to different data categories.

[0029] like Figure 2 As shown, in a further preferred embodiment, the value scoring process includes: after the original data is classified, for each type of original data, the data matrix construction module calls the evaluation dimension corresponding to that type of original data from the evaluation dimension library, and calls the core business objectives and quantitative indicators corresponding to that type of data from the business objective label library; through a differentiated feature extraction algorithm, real-time features related to the evaluation dimensions are extracted from each type of original data; based on a preset mapping rule, the matching degree between the real-time features and the corresponding quantitative indicators is calculated to obtain the original matching value of each evaluation dimension, and the original matching value is converted into a real-time score through a preset standardization algorithm; according to a preset business weight table, the real-time scores of each evaluation dimension are weighted and summed to obtain the value score of each data unit in that type of original data.

[0030] The business target tag library refers to a pre-built core business target-quantitative indicator association repository to match the value assessment needs of different data categories; the assessment dimension library refers to a pre-built data category-assessment dimension correspondence repository to accurately extract value features of different data categories; and the differentiated feature extraction algorithm refers to an algorithm that uses feature extraction logic matching the assessment dimension of different data categories to achieve category-adaptive feature extraction. The algorithm has a built-in mapping table between data categories and extraction logic, and calls different feature extraction logic for different data categories to ensure that the extracted real-time features accurately correspond to the assessment dimensions. For example, if the data category is social media interaction data, the algorithm calls the text semantic analysis + behavior duration statistics module to extract real-time features such as intentional keywords and interaction duration in the comment content; if the data category is e-commerce behavior data, the algorithm calls the path... The logic of path tracking and conversion node identification extracts real-time features such as the number of steps a user takes from browsing to adding to cart and the duration of stay after adding to cart. The preset business weight table refers to the correspondence between evaluation dimensions and weight coefficients. For example, if the data category is social media interaction data and the core business goal is to improve user conversion intention, then the weight of the interaction relevance dimension is set to 0.4, the weight of the interaction depth dimension is set to 0.3, and the weight of the interaction timeliness dimension is set to 0.3. If the core business goal is to improve user activity, then the weight of the interaction timeliness dimension is adjusted to 0.4, the interaction depth is set to 0.3, and the interaction relevance is set to 0.3. The formula for the standardization algorithm is: Standard score = (Original matching value - Minimum value of this dimension) ÷ (Maximum value of this dimension - Minimum value of this dimension) × 10. For example, if the original matching value of a data unit is 3, then the standard score = (3-0) ÷ (5-0) × 10 = 6 points.

[0031] Specifically, the mapping rules refer to the predefined correspondence rules between real-time features and quantitative indicators. These include: when the real-time feature is a comment containing keywords directly related to conversion, such as price, purchase link, or order method, the corresponding quantitative indicator is high-intent interaction, with an original matching value of 10; when the real-time feature is a comment involving product function details, usage methods, after-sales guarantees, or other consultative content, the corresponding quantitative indicator is medium-intent interaction, with an original matching value of 7; when the real-time feature is only interactive behavior without textual feedback, such as likes or favorites, the corresponding quantitative indicator is low-intent interaction, with an original matching value of 3; when the real-time feature is a comment containing generalized content without a clear inclination, such as "just passing by" or "taking a look," the corresponding quantitative indicator is weak-intent interaction, with an original matching value of 1; and when the real-time feature is no interactive behavior record, the corresponding quantitative indicator is invalid interaction, with an original matching value of 0.

[0032] The value scoring process involves first extracting real-time features from data units. For example, a user might post a 50-word comment on a social media platform two hours ago containing information about purchasing channels for a product. Next, based on preset mapping rules, corresponding quantitative indicators are matched and assigned original matching values. Since the user's comment contains conversion keywords like "purchasing channels," it scores 10 points in the interaction relevance dimension (high-intent interaction), 4 points in the timeliness dimension (interaction behavior within two hours), and 3 points in the interaction depth dimension (50-word long comment). Finally, a linear normalization algorithm is used to... The original matching values ​​of different dimensions are converted into standard scores of 0-10. The standard score for the timeliness dimension is calculated as (4-0) ÷ (4-0) × 10 = 10 points, and the standard score for the interaction depth dimension is calculated as (3-0) ÷ (3-0) × 10 = 10 points. Finally, the weighted sum is calculated according to the weight of each dimension in the business weight table. If the interaction relevance weight is 0.4, the timeliness weight is 0.3, and the interaction depth weight is 0.3, the total value score of the user data unit is 10 × 0.4 + 10 × 0.3 + 10 × 0.3 = 10 points.

[0033] The preferred embodiment of this invention provides a value scoring calculation process that achieves accurate quantification and unified value measurement of multi-dimensional interactive data through targeted extraction of real-time features, mapping rules matching business objectives, standardized algorithms with unified dimensions, and weight allocation aligned with business priorities. This solves the problem of difficulty in horizontally comparing the value of interactive data of different types and performances, while ensuring that the scoring results are highly correlated with the core business objective of improving user conversion intention, effectively avoiding interference from irrelevant data. Simultaneously, the quantified scoring results provide a clear basis for the differentiated processing of high, medium, and low-value data, prioritizing resources for high-value data and significantly improving the accuracy of data value identification and the targeting and efficiency of subsequent data processing.

[0034] Preferably, the value level includes three levels: high, medium, and low, and each value level corresponds to a value scoring range. The data matrix construction module determines the corresponding value level by matching the value score of each data unit with the value scoring range.

[0035] Among them, the high value level corresponds to a value score range of 80-100, the medium value level corresponds to a value score range of 50-79, and the low value level corresponds to a value score range of 0-49.

[0036] Specifically, in the value level matching process, the value scores of each data unit output by the data matrix construction module are first obtained. Each data unit's score is then compared with a preset range to determine its value level and assign a label. For example, a user interaction data unit with a value score of 85 is matched with a high value level, 62 with a medium value level, and 30 with a low value level. This process, through fixed and clear score range mapping, transforms abstract value scores into intuitive level labels, effectively avoiding resource allocation chaos caused by mixing high, medium, and low value data.

[0037] Preferably, the selection and differentiated adjustment of data processing strategies include: matching exclusive basic strategies for each type of raw data based on the core characteristics of each type of raw data in the data matrix; adding enhancement mechanisms to the basic strategies for high-value data units to strengthen feature extraction accuracy and processing priority; and reducing processing steps and compressing resource consumption ratios in the basic strategies for medium and low-value data units.

[0038] Specifically, the basic processing strategy refers to the initial processing scheme preset for the data classification labels in the data matrix, which is adapted to the core characteristics of that type of data; during the differentiated adjustment process, for high-value data units, the adjustment direction is to deepen the processing:

[0039] The basic processing strategy is enhanced by adding core steps. For example, the basic strategy for social media interaction data is sentiment labeling + keyword extraction. For high-value data, additional analysis of user history interaction correlation is added to link the user's similar interaction behavior in the past 30 days. The processing accuracy is improved, such as refining sentiment labeling from 3 levels to 5 levels and expanding keyword extraction from 5 core words to 10 related words. The processing time is extended to retain more detailed features, such as additional analysis of page jump trajectory after adding items to the cart for e-commerce behavior data.

[0040] For mid-value data units, the adjustment direction is to retain the basics and simplify appropriately: the core steps of the basic processing strategy are fully retained, and only non-critical steps are simplified. For example, social media interaction data still retains sentiment labeling and keyword extraction, but sentiment labeling remains at 3 levels of classification, and keyword extraction is reduced to 5 core words. E-commerce behavior data retains conversion node identification, but no longer analyzes the complete jump trajectory; only key nodes are labeled. The processing accuracy is consistent with the basic strategy, without increasing resource consumption.

[0041] For low-value data units, the adjustment direction is to significantly simplify the process: eliminate unnecessary steps in the basic processing strategy. For example, for social media interaction data, the keyword extraction step is directly eliminated, and only basic interaction scenario labeling is retained; for e-commerce behavior data, conversion node identification is eliminated, and only browsing time statistics are retained; processing accuracy is reduced to the minimum, such as no longer performing sentiment labeling on social media data, and only labeling valid / invalid interactions; and processing resource consumption is compressed, such as not consuming high-speed computing power, and only using idle resources for rapid batch processing.

[0042] The preferred embodiments of the present invention effectively solve the problem of resource allocation imbalance by adapting processing strategies according to data categories, adjusting and prioritizing the allocation of resources to high-value data based on value levels, thereby accurately matching processing depth with data value and improving the efficiency and accuracy of system resource utilization and data-driven operations.

[0043] Preferably, the layered extraction process includes: for the processed data, extracting deep behavioral features from high-value data units and basic attribute features from medium- and low-value data units; based on the data category information recorded in the data matrix, performing aggregation analysis on the two features of data units under the same data category to form a multi-dimensional feature subset; combining the characteristics of the target platform, assigning weights to the feature subsets of each dimension, and integrating the weighted feature subsets through a feature fusion algorithm to generate a user profile.

[0044] Among them, deep behavioral features refer to the basic attribute features of dynamic and refined behavioral information extracted from high-value data units that can reflect users' core needs, behavioral logic, or potential intentions; data features used to build the basic framework of user profiles and reflect users' inherent or stable attributes; aggregation analysis refers to the analysis method of classifying and summarizing scattered data units after hierarchical processing according to preset dimensions such as time, behavior type, and value level. For example, for high-value data units, aggregation by time dimension can count the total number and daily average number of such interactions in the past 7 days, and aggregation by behavior type dimension can distinguish the proportion of high-value behaviors such as comments and private message inquiries. Finally, the aggregated feature of 2 high-value interactions per day in the past 7 days, of which 60% are private message inquiries, is output; multi-dimensional feature subsets refer to the feature set of a certain business objective formed by screening and combining basic attribute features, aggregated features, and dynamic behavioral features based on data value level and target platform characteristics; feature fusion algorithm first converts non-numerical features into numerical labels, then calculates a comprehensive score for all features according to weights, and outputs a standardized profile feature vector.

[0045] Specifically, the weighting logic is as follows: For example, in the e-commerce platform feature subset, the number of high-value consultation interactions has the greatest impact on purchase conversion, with a weight of 0.4; the weight of add-to-cart category preference is set to 0.3; the weight of basic attribute - region is set to 0.2; and the weight of low-value interaction frequency is set to 0.1. In the social media platform subset, the weight of comment interaction duration is set to 0.4, the weight of content type preference is set to 0.3, and the weight of region is reduced to 0.1.

[0046] When generating user profiles, the intelligent profile generation module first extracts features from the layered data according to their value levels: it extracts in-depth behavioral features from high-value data units, including potential intent features reflecting user decision-making tendencies, temporal features embodying behavioral logic, and preference features detailing needs; simultaneously, it extracts basic attribute features from low- and medium-value data units. Then, based on the data category information recorded in the data matrix, it performs aggregate analysis on the in-depth behavioral features and basic attribute features within the same data category. For example, it aggregates the in-depth features of browsing and price inquiries for 1.5 HP air conditioners within the home appliance category with the basic features of second-tier cities and evening activity. By aggregating attribute features, structured information is generated about active users in second-tier cities during the evenings, who are interested in 1.5 horsepower air conditioners and value price. This information is then used to construct a multi-dimensional feature subset. Subsequently, based on the characteristics of the target platforms, such as e-commerce platforms focusing on conversion and social media platforms focusing on interaction, weights are assigned to the feature subsets. Deep behavioral features, which reflect the core needs of users, are given higher weights. Finally, through a feature fusion algorithm, non-numerical features are first converted into numerical labels, and then the core information of deep behavioral features and the key content of basic attribute features are integrated and calculated according to weights to generate a complete user profile that reflects both stable user attributes and accurately reflects their deep needs and behavioral logic.

[0047] The user profile generation process proposed in the preferred embodiment of the present invention extracts in-depth behavioral features from high-value data units and basic attribute features from medium- and low-value data units. Then, through feature aggregation analysis of the same category, weight allocation and feature fusion combined with platform characteristics, it solves the problems of insufficient accuracy and fragmented information in traditional profiles. The generated profile contains stable user attributes, highlights deep needs and behavioral logic, and is in line with the business goals of the target platform, providing accurate support for subsequent content generation.

[0048] like Figure 3As shown, preferably, the screening process of the high-value data unit set includes: extracting high-value data units from all data units; extracting user features associated with high-value data units based on the generated user profile; calculating the matching degree with the target platform characteristics based on the extracted data units and the attribute information corresponding to each data unit, and selecting data units with a matching degree higher than a preset threshold as high-value data units based on the matching data output by the hierarchical processing and scheduling module; verifying the processing precision identifier of high-value data units based on the processed data output by the hierarchical processing and scheduling module, and removing data units that fail the precision verification; and selecting other high-value data units of other categories that are strongly associated with the high-value data units based on the category association relationships recorded in the data matrix through a cross-category association algorithm, thus forming the final high-value data unit set.

[0049] Among them, user feature extraction refers to extracting core user features directly associated with high-value data units in the initial candidate set based on the user profile output by the intelligent profile generation module. For example, for a high-value data unit, it may be associated with features such as price sensitivity or high-frequency inquiries in the past 30 days. Matching degree calculation refers to performing feature matching between each high-value data unit in the initial candidate set and the target platform's feature tag library, combined with its attribute information, and calculating the matching degree score using the cosine similarity algorithm. The processing accuracy identifier is generated by the hierarchical processing module during the processing and reflects the completeness and accuracy of data feature extraction, such as high completeness and basic completeness. The processing accuracy verification only ensures... Data units marked as highly complete are retained, while data with incomplete feature extraction due to insufficient processing resources, such as high-value consultation data lacking key interaction timestamps, are removed to ensure the quality and reliability of the data in the set. Cross-category association filtering refers to filtering out other high-value data units in other categories that are strongly associated with the current high-value data unit in the user behavior chain through cross-category association algorithms. For example, a high-value social media consultation data can be associated with high-value e-commerce shopping cart data of the same user one hour later. Finally, a complete set of high-value data containing multiple categories and the entire chain is formed.

[0050] Taking e-commerce platform promotional activities as an example, the screening process for high-value data unit sets is as follows: First, data units with a value score of 80-100 points are extracted from the data matrix, including user A's comments on social media platforms asking if there are any discounts for this product during Double 11, user A's shopping cart addition records for a certain product on the e-commerce platform, and user A's private messages with customer service; then, features associated with these high-value data are extracted based on user A's profile; subsequently, for the target platform, the matching degree is calculated by combining the attribute information of the three data units, resulting in scores of 82, 95, and 90 respectively. Since the preset threshold is 80 points, all three pass the screening; next, the processing precision indicators of the three are checked and found to be of high completeness with no missing information, so all are retained; finally, through cross-category association algorithms, it is discovered that the social media comments and shopping cart addition records are only 10 minutes apart and point to the same product, while the customer service consultation occurs 30 minutes after adding to the cart, belonging to the same behavioral chain. Therefore, the three are associated and integrated to form the final high-value data unit set.

[0051] The preferred embodiments of this invention employ multiple filtering logics to ensure the high-value attributes of the data in the set, while also improving the data's business adaptability through platform matching and accuracy verification. Furthermore, cross-category association supplements the completeness of the user behavior chain, solving the problem of one-sided high-value data information from a single category. The resulting set of high-value data units accurately reflects core user needs and is highly compatible with the target platform, providing a high-quality data foundation for multi-version content generation and improving the accuracy of content matching with user needs.

[0052] like Figure 4 As shown, preferably, the generation of multi-version adapted content includes: extracting feature subsets and their corresponding weights from the user profiles of the intelligent profile generation module that correspond to the category to which the high-value data unit set belongs; dividing multiple adaptation scenarios according to the content format specifications of the target platform, and matching exclusive content generation templates for each adaptation scenario; for each adaptation scenario, filling the high-value data unit set into the corresponding template according to preset rules to generate multi-version adapted content.

[0053] Among them, content format specifications refer to the inherent rules established by the target platform for the presentation form, format standards, and style constraints of content; adaptation scenarios refer to the content display scenarios divided based on the target platform; and content generation templates refer to the structured content framework preset for each adaptation scenario.

[0054] Specifically, when dividing adaptation scenarios and matching exclusive templates according to the content format specifications of the target platform, it is necessary to first extract core dimensions such as format constraints, functional positioning, and interaction limitations from the specifications. Then, combined with user interaction nodes with the platform, such as browsing product detail pages and refreshing the homepage, the scenarios are divided into adaptation scenarios such as detail page pop-ups and homepage recommendation cards according to the interaction nodes and specification requirements. Subsequently, exclusive content templates are generated for each scenario—the templates reserve variables corresponding to high-value data and user characteristics. The preset rules for filling the template with high-value data unit sets include: data units associated with high-weight features (weight ≥ 60%) are displayed in full first; data units associated with medium-weight features (30% ≤ weight < 60%) are displayed in a simplified manner; and data units associated with low-weight features (weight < 30%) retain only the core fields. Ultimately, this rule ensures that the information displayed in the template not only meets the core needs of users but also avoids redundant content affecting the interactive experience, while providing a unified filling standard for content adapted to multiple versions.

[0055] The preferred embodiment of this invention solves the problems of content conflict with the platform, lack of focus in information, and lack of basis for version differences by matching the platform specifications to divide the scenario and generating multiple versions of content according to weight rules. This makes the content fit the platform ecosystem and highlight the core needs, improves the matching degree with user needs and the efficiency of interaction and conversion, and provides a comparable sample for subsequent optimization.

[0056] Preferably, it also includes a feedback optimization module, which is used to collect user feedback data on the adapted content of each version, select the optimal generation logic based on the feedback results, and guide the generation of subsequent adapted content.

[0057] Specifically, the feedback optimization module uses platform tracking technology to collect real-time user interaction feedback data on content adapted to different versions, such as click count, dwell time, and close rate. It also connects to the backend conversion statistics interface to obtain in-depth conversion data such as order placement and coupon redemption. The feedback data is then categorized and analyzed according to the adaptation scenario and user characteristics. The interaction rate and conversion efficiency of different versions of content are compared. For example, in the details page pop-up scenario, the content version that targets price-sensitive users and adopts the rule of displaying high-weight discount information in its entirety and medium-weight category information in a simplified manner is selected. The generation logic corresponding to this version is determined as the optimal generation logic. Subsequently, this logic is updated synchronously to the content generation module as the default standard for content generation under similar scenarios and user characteristics. For example, the visual priority of the "discount information" variable in the template is adjusted, and the execution rule of "weight ≥ 60% full display" is solidified. Ultimately, this solves the problems of lagging feedback in traditional content generation and lack of data support for optimization.

[0058] In summary, the matrix-style data hierarchical intelligent management system provided by the preferred embodiment of the present invention achieves cross-platform data standardization preprocessing through a multi-source data acquisition module, and completes data classification and value grading through a data matrix construction module, laying the foundation for precise management. Then, the hierarchical processing and scheduling module allocates resources differentiated according to value level, improving the processing priority and resource utilization of high-value data. The intelligent profile generation module combines the deep behavioral characteristics of high-value data with the basic attribute characteristics of medium- and low-value data to generate accurate profiles that fit the platform characteristics and reflect the deep needs of users, solving the problem of insufficient accuracy in traditional profiles. The multi-version content generation module generates content that highlights key points and has strong adaptability based on weight rules and platform specifications, avoiding information redundancy and platform conflicts. The feedback optimization module forms a closed loop of generation-feedback-optimization, promoting dynamic iteration of strategies. Ultimately, it comprehensively solves the pain points of inefficient data management, vague profiles, poor content adaptability, and lagging optimization, significantly improving resource utilization efficiency, user profile accuracy, content-user demand matching degree, and interaction conversion efficiency, while strengthening the system's adaptability to different platforms and user preferences.

[0059] The above description is merely a preferred embodiment of the technical solution of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A matrix-style hierarchical intelligent data management system, characterized in that, The matrix-style hierarchical intelligent data management system includes: The multi-source data acquisition module includes a cross-platform data interface and a preprocessing unit. The cross-platform data interface obtains users' historical interaction data on multiple platforms by connecting to the official data APIs of each platform. The preprocessing unit is used to unify the format and clean up anomalies in the historical interaction data, and output standardized raw data. The data matrix construction module is used to classify the standardized raw data, score the value of each data unit in each category of raw data, match the corresponding value level according to the value score of each data unit, and construct a data matrix by combining the attributes of each data unit. The hierarchical processing and scheduling module is used to select the basic processing strategy corresponding to each type of raw data based on the data matrix. It is also used to differentiate the basic processing strategy according to the value level of the data unit. Specifically, for high-value data units, an enhancement mechanism is added to the basic strategy to strengthen the feature extraction accuracy and processing priority; for medium and low-value data units, the processing steps are reduced and the resource consumption ratio is compressed in the basic strategy. The hierarchical processing and scheduling module prioritizes the allocation of processing resources to high-value data units and outputs the processed data. The intelligent profile generation module is used to extract profile features from the processed data and generate user profiles by combining them with the characteristics of the target platform. The multi-version content generation module is used to filter a set of high-value data units that match the characteristics of the target platform based on the value level and data category of the data units recorded in the data matrix, and generate multi-version adapted content in combination with user profiles.

2. The matrix-style hierarchical intelligent data management system according to claim 1, characterized in that, The matrix-style data hierarchical intelligent management system pre-stores a business target label library and an evaluation dimension library. The business target label library contains core business targets and quantitative indicators corresponding to different data categories, and the evaluation dimension library contains evaluation dimensions corresponding to different data categories.

3. The matrix-style hierarchical intelligent data management system according to claim 2, characterized in that, The value scoring process includes: After the raw data is classified, for each type of raw data, the data matrix construction module calls the evaluation dimensions corresponding to that type of raw data from the evaluation dimension library, and calls the core business objectives and quantitative indicators corresponding to that type of data from the business objective label library. By using a differential feature extraction algorithm, real-time features related to the evaluation dimension are extracted from various types of raw data; Based on the preset mapping rules, the matching degree between real-time features and corresponding quantitative indicators is calculated to obtain the original matching value of each evaluation dimension, and the original matching value is converted into a real-time score through a preset standardization algorithm. Based on the preset business weight table, the real-time scores of each evaluation dimension are weighted and summed to obtain the value score of each data unit in this type of raw data.

4. The matrix-based hierarchical intelligent data management system according to claim 1, characterized in that, The value levels include three levels: high, medium, and low, and each value level corresponds to a value scoring range. The data matrix construction module determines the corresponding value level by matching the value score of each data unit with the value scoring range.

5. The matrix-style hierarchical intelligent management system for data as described in claim 1, characterized in that, The layered extraction process includes: For the processed data, deep behavioral features are extracted from high-value data units and basic attribute features are extracted from medium- and low-value data units. Based on the data category information recorded in the data matrix, the two features of data units under the same data category are aggregated and analyzed to form a multi-dimensional feature subset; Based on the characteristics of the target platform, weights are assigned to feature subsets of each dimension, and the weighted feature subsets are integrated through a feature fusion algorithm to generate user profiles.

6. The matrix-style hierarchical intelligent data management system according to claim 1, characterized in that, The screening process for the high-value data unit set includes: Extract high-value data units from all data units; Based on the generated user profiles, extract user features associated with high-value data units; Based on the extracted data units, and combined with the attribute information corresponding to each data unit, the matching degree with the characteristics of the target platform is calculated, and data units with a matching degree higher than a preset threshold are selected as high-value data units. Based on the processed data output by the hierarchical processing and scheduling module, the processing accuracy identifier of high-value data units is verified, and data units that fail the accuracy verification are removed. Based on the category relationships recorded in the data matrix, a cross-category association algorithm is used to filter out other high-value data units that are strongly associated with high-value data units, forming the final set of high-value data units.

7. The matrix-style hierarchical intelligent management system for data as described in claim 5, characterized in that, The generated multi-version adaptation content includes: From the user profile generated by the intelligent profile generation module, extract the feature subsets corresponding to the category to which the high-value data unit set belongs and their corresponding weights. Based on the content format specifications of the target platform, we divide the scenarios into multiple adaptation scenarios and match exclusive content generation templates for each scenario. For each adaptation scenario, a set of high-value data units is populated into the corresponding template according to preset rules to generate multi-version adapted content.

8. The matrix-style hierarchical intelligent data management system according to claim 1, characterized in that, It also includes a feedback optimization module, which collects user feedback data on the adapted content for each version, selects the optimal generation logic based on the feedback results, and guides the generation of subsequent adapted content.

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