Advertisement sdk putting method and system based on multi-dimension user portrait

Abstract

The application discloses an advertisement SDK putting method and system based on multi-dimensional user portrait, and belongs to the technical field of advertisement SDK putting, and comprises the following steps: data acquisition, feature construction, dynamic updating, strategy matching and reverse optimization.The application collects multi-dimensional feature data, constructs a user panoramic feature set through normalization preprocessing and weighted fusion, collects user advertisement incremental interaction data in real time through an SDK listening component, dynamically updates the feature weight and fusion result of each dimension after a threshold is triggered, then matches optimal advertisement content relying on the updated panoramic feature, adaptively adjusts the putting weight, exposure frequency, pushing time period and display style to generate a target putting strategy, synchronizes data through a standardized interface, generates high-concurrency putting instructions through distributed asynchronous lightweight reasoning, finally collects full-cycle putting effect data to reversely iteratively optimize the feature weight and matching rule, and forms a closed-loop putting optimization mechanism.

Description

Technical Field

[0001] This invention relates to the field of advertising SDK delivery technology, and in particular to an advertising SDK delivery method and system based on multi-dimensional user profiles. Background Technology

[0002] In the actual business process of intelligent cockpit systems in new energy vehicles (such as in-vehicle central control screens equipped with the Android Automotive operating system), third-party applications typically push content to vehicle occupants through built-in advertising SDKs.

[0003] Existing in-vehicle advertising SDKs (Software Development Kits) suffer from the following core technical defects and limitations in feature processing and delivery strategy matching: Firstly, they suffer from single-dimensional feature dimensions and outdated data. Existing solutions typically rely solely on static device models or rough geographical locations for content delivery, failing to collect and integrate multi-dimensional user behavior characteristics within the vehicle (such as single ad dwell time and negative feedback behavior) and delivery scenario characteristics (such as screen orientation, network environment, and vehicle parking / charging status). This singular data collection method leads to a severe disconnect between the pushed content and the user's actual current needs. Secondly, they lack a dynamic feature weight update mechanism. Existing solutions cannot capture behavioral changes and trigger dynamic adjustments to feature weights in real time when users interact with in-vehicle ads. When users generate new interactive behaviors or negative feedback while driving, the system still uses historical static feature data for matching, easily leading to accidental touches or frequent closures during driving, reducing conversion efficiency and potentially affecting driving safety. Thirdly, the in-vehicle device's high-concurrency feature processing capabilities are insufficient. Existing advertising SDKs often employ a centralized synchronous processing architecture when performing feature matching. During peak vehicle startup periods or when network environments frequently switch, this architecture cannot achieve millisecond-level feature data synchronization and lightweight inference, easily leading to excessive resource consumption and increased computational latency in the vehicle's infotainment system, thereby affecting the normal rendering of advertisements and the core in-vehicle interactive experience. There is an urgent need in this field for a technical solution that can achieve accurate, real-time, and low-latency matching and delivery of advertising content in in-vehicle scenarios through full-scale multi-dimensional feature collection, dynamic weight fusion, and lightweight distributed processing, without user classification or tagging. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide an advertising SDK delivery method and system based on multi-dimensional user profiles. This system can collect multi-dimensional feature data, perform normalization preprocessing and weighted fusion to construct a comprehensive user feature set; then, through an SDK monitoring component, collect incremental user advertising interaction data in real time, dynamically updating the feature weights and fusion results of each dimension after a threshold is triggered; subsequently, based on the updated comprehensive features, match the optimal advertising content, adaptively adjust delivery weights, exposure frequency, push time periods, and display styles to generate a target delivery strategy; synchronize data through standardized interfaces and generate high-concurrency delivery instructions through distributed asynchronous lightweight inference; finally, collect full-cycle delivery performance data to iteratively optimize feature weights and matching rules, forming a closed-loop delivery optimization mechanism.

[0005] This invention provides an advertising SDK delivery method based on multi-dimensional user profiles. The method includes: Step 1, collecting user basic information features, user behavior features, user interest and preference features, user consumption habit features, and delivery scenario features to obtain full multi-dimensional user feature data, and performing normalization preprocessing operations to perform weighted fusion calculations on the features of each dimension to obtain a multi-dimensional user panoramic feature set; Step 2, based on the multi-dimensional user panoramic feature set, capturing all user interaction behaviors with advertisements in real time and incrementally collecting user behavior change data, and adjusting the weight allocation of each dimension feature and the feature fusion result when the user behavior change data triggers a preset update threshold to obtain a real-time updated multi-dimensional user panoramic feature set; Step 3, based on the real-time update... The newly compiled multi-dimensional user panoramic feature set is used to match the most suitable advertising content through a mapping matching model, and the advertising placement weight, exposure frequency, push time period and display style are adjusted simultaneously to obtain the target placement strategy; Step four, according to the target placement strategy, the feature data and placement strategy are synchronized through a standardized interface, and a distributed asynchronous processing architecture is used to perform lightweight inference optimization on the feature analysis model to obtain the advertising precise placement execution instructions in a high-concurrency environment; Step five, after the advertising precise placement execution instructions are executed, the full-cycle advertising performance data is obtained, the impact weight of different feature dimensions on advertising conversion efficiency is quantified, and the placement performance data is back-input into the feature construction model and strategy matching model to obtain the optimized feature weight allocation and matching rules.

[0006] This application also provides an advertising SDK delivery system based on multi-dimensional user profiles. This system is applied to an advertising SDK delivery method based on multi-dimensional user profiles. The system includes: a data acquisition module, a feature construction module, a dynamic update module, a strategy matching module, and a reverse optimization module. The data acquisition module collects user basic information features, user behavior features, user interest and preference features, user consumption habit features, and delivery scenario features to obtain full multi-dimensional user feature data. It then performs preprocessing operations such as cleaning, deduplication, outlier removal, and normalization, and performs weighted fusion calculations on the features of each dimension to obtain a multi-dimensional user panoramic feature set. The feature construction module, based on the multi-dimensional user panoramic feature set, captures all user-advertisement interaction behaviors in real time and incrementally collects user behavior change data. When user behavior change data triggers a preset update threshold, it adjusts the weight allocation and feature characteristics of each dimension. The feature fusion results yield a real-time updated multi-dimensional user panoramic feature set. The dynamic update module, based on this set, uses a mapping matching model to match the most suitable ad content and simultaneously adjusts ad placement weights, exposure frequency, push time periods, and display styles to obtain a target placement strategy. The strategy matching module, based on the target placement strategy, synchronizes feature data with the placement strategy through a standardized interface and employs a distributed asynchronous processing architecture to perform lightweight inference optimization on the feature analysis model, resulting in precise ad placement execution instructions under high concurrency. The reverse optimization module, after executing the precise ad placement instructions, obtains full-cycle ad performance data, quantifies the impact weights of different feature dimensions on ad conversion efficiency, and inputs the performance data back into the feature construction model and strategy matching model to obtain optimized feature weight allocation and matching rules.

[0007] Beneficial effects The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: First, addressing the pain points of single feature dimensions and lagging data, this embodiment collects user basic information features, user behavior features, user interest and preference features, user consumption habit features, and deployment scenario features, and performs weighted fusion calculations on the features of each dimension to obtain a multi-dimensional panoramic user feature set. This step avoids the limitations of traditional solutions that rely on single static data, achieves comprehensive coverage and accurate quantification of feature dimensions, and ensures that the feature set can objectively and comprehensively reflect the user's real state and needs in the in-vehicle environment.

[0008] Secondly, addressing the pain point of lacking a dynamic feature weight update mechanism, this embodiment uses a built-in monitoring component in the SDK to capture all user interactions with advertisements in real time and incrementally collect user behavior change data. When user behavior changes trigger a preset update threshold, the weight allocation of each dimension feature and the feature fusion result are automatically adjusted to obtain a real-time updated multi-dimensional user panoramic feature set. This mechanism completely solves the problem of feature data lag, ensuring that the targeted delivery strategy can respond to the user's real-time status changes inside the vehicle in milliseconds, effectively reducing the probability of negative feedback behavior.

[0009] Third, addressing the pain point of insufficient high-concurrency feature processing capabilities on the in-vehicle infotainment system, this embodiment achieves synchronization of feature data and delivery strategies through a standardized interface, and employs a distributed asynchronous processing architecture to perform lightweight inference optimization on the feature analysis model, obtaining accurate ad delivery execution instructions under high-concurrency environments. This architecture significantly reduces the computational latency of the feature matching process, ensuring the real-time performance of delivery strategy adjustments in complex in-vehicle network environments, without affecting the normal rendering of the in-vehicle system and the user interaction experience.

[0010] Fourth, addressing the pain point of the inability to optimize campaign performance in a closed loop, this embodiment collects campaign performance data throughout the entire campaign lifecycle, quantifies the impact weight of different feature dimensions on ad conversion efficiency, and then inputs the campaign performance data back into the feature building model and strategy matching model to obtain optimized feature weight allocation and matching rules. This step forms a complete closed loop of data-driven continuous optimization of feature weights, providing advertisers with a practical optimization basis and significantly improving the SDK's refined operation capabilities and campaign conversion efficiency. Attached Figure Description

[0011] Figure 1 Flowchart of the advertising SDK delivery method based on multi-dimensional user profiles provided in this application embodiment; Figure 2 A flowchart illustrating the target delivery strategy of the advertising SDK delivery method based on multi-dimensional user profiles provided in this application embodiment; Figure 3 This is a schematic diagram of the structure of the advertising SDK delivery system based on multi-dimensional user profiles provided in the embodiments of this application. Detailed Implementation

[0012] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0013] like Figure 1 The diagram shown is a flowchart of an advertising SDK delivery method based on multi-dimensional user profiles provided in this application embodiment. The method includes the following steps: Step 1: Collect user basic information features, user behavior features, user interest and preference features, user consumption habit features, and advertising scenario features to obtain full data of multi-dimensional user features. Perform normalization preprocessing operation and perform weighted fusion calculation on the features of each dimension to obtain a multi-dimensional user panoramic feature set.

[0014] It should be further explained that the specific process for obtaining the multi-dimensional user panoramic feature set is as follows: Based on the user terminal's operating status, data such as age, gender, region, device model, system version, network environment, and terminal type are collected to obtain basic user information feature data. Based on the user terminal's operation records, collect ad interaction records, click trajectories, single ad dwell time, ad revisit frequency, and in-app page jump paths to obtain user behavior characteristic data. Based on the interaction records in the user behavior feature data, we collect the types of advertisements with high-frequency interactions, content focus, click preferences, and negative feedback behaviors to obtain user interest preference feature data. Based on historical transaction and conversion records, we collect data on consumption levels, historical advertising conversion records, potential consumption tendencies, and payment frequency to obtain user consumption habit characteristics data. Based on the current advertising environment, data on advertising time periods, user app usage scenarios, terminal screen orientation (portrait or landscape), and network environment are collected to obtain advertising scenario characteristic data.

[0015] In this embodiment, multi-dimensional feature data collection is performed using the data collection unit built into the advertising SDK to construct a multi-dimensional user panoramic feature set. First, user basic information feature data is collected based on the real-time operating status of the user terminal. The SDK automatically reads the terminal's underlying operating parameters and device configuration information, acquiring user age, gender, residential region, device model, operating system version, current network type and speed, and terminal type information such as whether it is a mobile phone, tablet, or computer. All of the above information is integrated and summarized to form complete user basic information feature data. Second, the real-time operation records of the user terminal are continuously monitored, tracking all user behaviors within the application. This includes accurately collecting user interactions such as clicking, browsing, and closing ads; click trajectories formed by finger swipes and page clicks; page dwell time for a single ad from opening to exiting; ad revisit frequency when the same ad is accessed multiple times; and in-app page jump paths generated when switching between different functional pages and ad pages within the application. User behavior feature data is obtained by integrating the above behavioral information. Furthermore, the interaction records in the collected user behavior data are analyzed in depth to statistically analyze users' high-frequency clicks, the types of ads they browse, the content they consistently follow, and their preferred click categories. Negative feedback behaviors such as blocking, skipping, and complaining about ads are also recorded. Based on this, user interest and preference characteristics are summarized. Subsequently, historical transaction orders and ad conversion records stored in the system's backend are retrieved to statistically categorize user consumption levels, organize historical ad conversion records of users who clicked ads and completed orders and payments, and analyze users' potential consumption tendencies by combining browsing, favorites, and add-to-cart behaviors. The frequency of payments within a fixed period is also statistically analyzed, ultimately integrating user consumption habit characteristics data. Finally, ad placement scenario characteristic data is collected in conjunction with the current overall ad placement environment. This includes accurately recording the current ad placement time, the specific usage scenario of the application, the current landscape or portrait display status of the mobile phone or tablet, and network environment information such as network signal strength and network access method. All this information is then aggregated to obtain ad placement scenario characteristic data.

[0016] It should be further explained that the multi-dimensional user panoramic feature set also includes: Based on user basic information feature data, user behavior feature data, user interest and preference feature data, user consumption habit feature data, and advertising scenario feature data, the data is aggregated to obtain a full set of multi-dimensional user feature data. Based on the full dataset of multi-dimensional user features, preprocessing operations such as cleaning, deduplication, outlier removal, and normalization are performed to filter out invalid and dirty data, resulting in preprocessed feature data. Based on the preprocessed feature data, the numerical vectors of each dimension feature are extracted, and the numerical vectors of each dimension feature are weighted and fused to obtain the weight allocation results of each dimension feature. Based on the weight allocation results, the weighted and fused feature vectors of each dimension are concatenated and combined to obtain the multi-dimensional feature vector set of the current user. By structurally encapsulating the multidimensional feature vector set, a multidimensional user panoramic feature set is obtained.

[0017] In this embodiment, the five independent dimensional feature data collected above are comprehensively aggregated and integrated. Basic attribute data, behavioral trajectory data, interest preference data, consumption level data, and real-time scenario environment data, scattered across the terminal SDK cache, business service backend, delivery strategy database, and user behavior log system, are uniformly gathered, aligned, and merged. This covers five major aspects: user static inherent attributes, dynamic behavioral performance, subjective interest tendencies, objective consumption capacity, and real-time delivery environment, forming a complete, multi-dimensional, and time-series-consistent full-volume user feature data set, thoroughly... To avoid the problems of one-dimensional, scattered, and missing dimensions in single-dimensional data, a standardized data preprocessing mechanism is automatically executed after acquiring the full set of feature data. This mechanism performs deep cleaning, precise deduplication, outlier removal, and global normalization on the original full set of data. Specifically, this includes removing duplicate and redundant data caused by terminal lag, network jitter, and repeated reporting; filtering out interference and dirty data caused by accidental touch operations, extremely short invalid pauses, and abnormal jumps; removing extreme outlier data that exceeds the normal data range; and uniformly normalizing and scaling various feature data with different units, value ranges, and statistical calibers to ensure that all dimensions are normalized and scaled. The system maps feature data to the same standard numerical range to eliminate the bias caused by differences in dimensional units in subsequent feature fusion calculations, ultimately obtaining preprocessed feature data that is formatted correctly, numerically valid, accurate, and free of redundant interference. Based on this, the system performs refined feature parsing and vector extraction on the preprocessed standard feature data, numerically encoding and quantizing the feature information of user basic dimensions, behavior dimensions, interest dimensions, consumption dimensions, and scenario dimensions, generating independent feature numerical vectors for each dimension. Simultaneously, combining preset feature importance evaluation rules and historical conversion contribution, the system performs differentiated weighted fusion calculations on the feature numerical vectors of each dimension, assigning corresponding feature weights based on the contribution of different features to ad matching, user conversion, and ad placement adaptation, accurately outputting the quantified weight allocation results for each dimension. Subsequently, based on the determined weight allocation results, the system performs ordered splicing, vector fusion, and dimensional combination of the weighted feature numerical vectors of each dimension, breaking down data silos between feature dimensions and integrating discrete single-dimensional vectors into a complete multi-dimensional feature vector set that is interconnected, dimensionally complementary, and information-linked, fully characterizing the user's static attributes, dynamic behaviors, interests, spending power, and real-time scenario status.Finally, the system standardizes and structures the generated multidimensional feature vector set, unifying the data storage format, vector dimension order, feature identifier fields, and calling interface specifications. This completes the structured, serialized, and modular integration of feature data, ultimately constructing a multidimensional user panoramic feature set that is dimensionally comprehensive, highly pure, weighted, highly correlated, and capable of real-time computation. This provides accurate, stable, and standardized core feature data support for subsequent dynamic monitoring of user behavior, iterative updates of feature weights, intelligent matching of advertising materials, and generation of refined delivery strategies.

[0018] Step 2: Based on the multi-dimensional user panoramic feature set, the SDK's built-in listening component captures all user interactions with advertisements in real time and incrementally collects user behavior change data. When user behavior change data triggers a preset update threshold, the weight allocation of each dimension feature and the feature fusion result are adjusted to obtain the real-time updated multi-dimensional user panoramic feature set.

[0019] It should be understood that the specific process for adjusting the weight allocation of features across various dimensions and the feature fusion result is as follows: Based on a multi-dimensional set of user panoramic features, the SDK's built-in listening component captures all user interactions with advertisements in real time, resulting in incremental user behavior change data. Based on incremental user behavior change data, the magnitude of behavior change is calculated, and it is determined whether the magnitude of behavior change triggers a preset update threshold. The magnitude of behavior change is used to characterize the degree of deviation between real-time incremental behavior and historical baseline behavior in advertising interaction scenarios. Based on the judgment result of triggering a preset update threshold according to the magnitude of behavioral change, the feature update mechanism is automatically activated to adjust the weight allocation of features in the corresponding dimension and the feature fusion result, specifically: If the magnitude of the behavioral change is greater than or equal to the preset update threshold, it is determined to be a trigger condition for updating. The user's current behavior deviates from the historical profile features, and the original feature weight allocation and feature fusion results cannot accurately match the user's latest status. If the change in behavior is less than the preset update threshold, it is determined that the update condition has not been triggered, the user's advertising interaction behavior and interest preferences are normal, and the original weights and fusion results remain unchanged.

[0020] The specific process for automatically starting the feature update mechanism is as follows: Based on the preset behavioral dimension weight mapping rules and the quantified behavioral change magnitude, the weights of user behavior features, user interest and preference features, user consumption habits, and advertising scenario features are dynamically adjusted using gradients. Specifically: The weights of the behavioral features corresponding to the user's new positive interaction behavior are positively gained and adjusted. The magnitude of the current behavior change is input into the behavior dimension weight mapping rule, and the weights of the user's behavior features after gain are output. The weights of emerging user interest and preference features are positively adjusted by inputting the magnitude of the current behavioral change into the behavioral dimension weight mapping rule, and the weights of the user interest and preference features after gain are output. The consumption habit features corresponding to the user's current incremental consumption behavior are positively adjusted, the magnitude of the current behavior change is input into the behavior dimension weight mapping rule, and the weight of the user's consumption habit features after the gain is output. For delivery scenario features with high behavioral change amplitude, positive gain adjustment is performed. The current behavioral change amplitude is input into the behavioral dimension weight mapping rule, and the weight of the delivery scenario feature after gain is output. Based on the adjusted weights of user behavior features, user interest and preference features, user consumption habits, and advertising scenarios, normalization is performed to generate a panoramic user feature result that adapts to the latest user behavior status and fits the real-time advertising scenario.

[0021] In this embodiment, after constructing the multi-dimensional user panoramic feature set, the user profile data is not permanently fixed. Instead, the built-in listening component of the advertising SDK deployed on the client side is used to achieve continuous and full-scale dynamic monitoring of user advertising interaction behavior. This enables adaptive iterative updates of feature weight allocation and feature fusion results across various dimensions. Using the constructed multi-dimensional user panoramic feature set as the historical baseline profile, the SDK's underlying listening thread captures in real time all user interactions with various advertising materials, pop-up ads, news feed ads, video ads, and card ads within the front-end application, covering ad exposure, clicks, etc. This system continuously collects all new user behavior data generated during the real-time campaign period, including all types of positive, neutral, and negative behaviors such as clicking, swiping, long-pressing, page dwell, redirecting, adding to cart / favorites, inquiries / conversions, skipping / closing, and blocking complaints. Through data deduplication, time-series filtering, and incremental differential calculation, it eliminates historically recorded duplicate behavior data and accurately extracts incremental user behavior change data that represents the latest user behavior dynamics, achieving real-time perception of user behavior changes. Based on this, a preset behavior offset quantification algorithm is used to perform deep calculations on the incremental user behavior change data to accurately calculate the user's current... The magnitude of behavioral change in the user's current advertising interaction behavior relative to the historical steady-state baseline behavior objectively and quantitatively characterizes the overall deviation, preference change intensity, and behavioral activity fluctuation of the user's real-time incremental behavior in the advertising interaction scenario compared to the historical baseline behavior. This provides a quantitative basis for subsequent feature update judgment. Subsequently, the calculated behavioral change magnitude is compared and verified with the system's preset feature update threshold to complete the intelligent update condition judgment. In the specific judgment implementation, when the detected behavioral change magnitude is greater than or equal to the system's preset update threshold, it is determined that the user's recent advertising interaction behavior, interest tendency, consumption intention, or scenario usage status has significantly deviated from the historical profile. The original fixed feature weight allocation ratio and feature fusion result can no longer accurately adapt to the user's latest real behavioral status and preference needs, resulting in a profile lag bias. The system automatically triggers and starts the feature dynamic update mechanism. Conversely, when the behavioral change magnitude is less than the preset update threshold, it is determined that the user's current advertising interaction behavior, interest preference, and consumption habits are generally within the steady-state fluctuation range, with no significant preference deviation. There is no need to correct the feature weights and fusion results, and the original feature weight allocation scheme and panoramic feature fusion result remain unchanged.When the feature update mechanism is triggered, the system strictly follows the pre-trained and configured behavior dimension weight mapping rules, using the quantified real-time behavior change amplitude as the core control coefficient. It performs refined and differentiated gradient dynamic adjustments to the weights of four core dimensions: user behavior feature weights, user interest preference feature weights, user consumption habit feature weights, and ad placement scenario feature weights. Specifically: For the behavior feature dimensions corresponding to recent positive incremental interaction behaviors such as new clicks, long-term browsing, and proactive interaction, the system calculates positive gain based on the current behavior change amplitude using the weight mapping rules, dynamically increasing the weight ratio of effective behavior features and outputting the gain-optimized user behavior feature weights; For the interest feature dimensions corresponding to recent high-frequency attention, proactive browsing, and continuous interaction with emerging advertising content and category preferences, the system updates the preference weights based on the behavior change amplitude, strengthening the priority of the user's latest interest tags and outputting the updated user interest preference feature weights; For the consumption habit feature dimensions corresponding to recent incremental consumption behaviors such as new purchases, orders, payments, and price comparison inquiries, the system performs positive gain updates on the preference weights, strengthening the weight priority of the user's latest interest tags and outputting the updated user interest preference feature weights; For the consumption habit feature dimensions corresponding to recent incremental consumption behaviors such as new purchases, orders, payments, and price comparison inquiries, the system performs positive gain updates on the preference weights based on the behavior change amplitude, strengthening the weight priority of the user's latest interest tags and outputting the updated user interest preference feature weights; Simultaneously, the system dynamically adjusts the weights of consumption characteristics based on the magnitude of behavioral changes, updating the weight parameters to better reflect users' latest consumption intentions and capabilities, resulting in optimized weights for user consumption habits. For real-time advertising scenarios with high frequency of incremental behavior, good interaction effects, and strong user activity, the system increases the weight proportion of corresponding scenario features based on the magnitude of behavioral changes, weakening the influence of inefficient and poorly adapted scenarios, and outputting scenario feature weights adapted to the real-time environment. After independently and dynamically optimizing the weights of the four major feature dimensions, the system performs global normalization on all updated feature weight parameters to eliminate extreme biases in single-dimensional weights, ensuring a balanced and compliant weight distribution across dimensions. Finally, based on the new weight allocation results, the system recalculates the multi-dimensional feature fusion, generating a new panoramic user feature result that accurately reflects users' latest behavioral preferences, fits the real-time advertising scenario, and eliminates historical profile lag biases. This upgrades user profiles from static, fixed displays to dynamic, adaptive iterations, providing real-time, effective, and accurate user feature support for subsequent precise ad matching and refined advertising strategies.

[0022] Step 3: Based on the real-time updated multi-dimensional user panoramic feature set, the most suitable advertising content is matched through the mapping matching model, and the advertising placement weight, exposure frequency, push time period and display style are adjusted simultaneously to obtain the target placement strategy.

[0023] It should be understood that Figure 2The specific flow of the target delivery strategy flowchart for the advertising SDK delivery method based on multi-dimensional user profiles provided in this application embodiment is as follows: Starting with the real-time updated multi-dimensional user panoramic feature set, the system first extracts the multi-dimensional feature vector data of the current user, and then performs similarity calculation and matching in the mapping matching model based on the preset feature-material-strategy association rules to obtain three preliminary matching results: high, medium, and low. The system implements differentiated parameter control for different matching levels: for high similarity matching results, the delivery weight is increased by positive gain, the exposure frequency is increased, the push is concentrated in the optimal active period, and a high-conversion display style is adopted; for medium similarity matching results, the baseline delivery weight and exposure frequency are maintained, and the push is tested in alternative time periods and a regular display style is adopted; for low similarity matching results, the invalid delivery is reduced by gradient weight reduction, tightening the exposure limit, lengthening the exposure interval, and adopting a low-interference display style. The adjusted delivery weight, exposure frequency, push period, and display style are synchronously and collaboratively optimized and coupled to generate a target delivery strategy that adapts to the real-time user characteristics and delivery scenario.

[0024] It should be noted that the specific process for obtaining the target delivery strategy is as follows: Based on the real-time updated multi-dimensional user panoramic feature set, extract the multi-dimensional feature vector of the current user to obtain the current user feature vector data; Based on the current user feature vector data, similarity calculation and matching are performed in the mapping matching model through preset feature-material-strategy association rules to obtain preliminary matching results, including high similarity matching results, medium similarity matching results and low similarity matching results; Based on the initial matching results, the ad placement weight, exposure frequency, push time period, and display style will be adjusted accordingly, specifically as follows: The preliminary matching results are structured and analyzed to extract similarity scores between user features and advertising materials and delivery strategies, matching fit of features in each dimension, scene adaptability level and preference matching tags, and to build a quantitative matching evaluation system to achieve accurate correlation mapping between matching results and delivery parameters. Based on the similarity level of the initial matching results, the advertising placement weight is dynamically adjusted. If the matching result is high similarity, it is determined that the current advertising material is highly compatible with user behavior characteristics, interests, consumption habits and placement scenarios. The traffic competition weight, ranking display weight and strategy optimization weight of the advertisement are positively increased to improve the priority of the advertisement in the candidate placement pool.

[0025] The target delivery strategy also includes: If the matching result is of medium similarity, the basic advertising weight is maintained. If the matching result is of low similarity, the advertising weight is reduced in a gradient manner to lower the traffic allocation priority of unsuitable ads and achieve precise allocation of traffic resources. The frequency of ad exposure is adaptively adjusted based on the initial matching results, specifically as follows: For high-match ads, increase the effective exposure frequency per unit time and strengthen the user reach density of high-quality, well-matched creatives; for medium-match ads, adopt a uniform interval exposure mode to maintain the baseline exposure frequency; for low-match ads and ads with negative match characteristics, tighten the exposure frequency limit, lengthen the exposure interval, and reduce ineffective and inefficient exposures. If the result is a high match, prioritize targeted push during the most active time period and reduce the proportion of push during non-core time periods; if the result is a medium or low match, only retain alternative time periods for trial push, avoiding inefficient time periods when users are silent or inactive. After completing the synchronous and collaborative optimization of the placement weight, exposure frequency, push time period, and display style, all parameters are coupled and verified to generate a target placement strategy that adapts to the real-time characteristics of users and fits the placement scenario.

[0026] In this embodiment, after completing the real-time dynamic update of multi-dimensional user panoramic features, the system will adaptively generate intelligent matching and refined targeting strategies based on the latest iterative user profile data. The system will perform structured analysis and feature extraction on the real-time updated multi-dimensional user panoramic feature set. From the fused panoramic feature data, standardized multi-dimensional feature vectors corresponding to user basic attributes, historical behavior, interests, consumption habits, and real-time scenarios will be precisely extracted and decomposed. After field filtering, vector alignment, and data normalization, current user feature vector data that can be directly input into the model will be generated, ensuring that the input data is dimensionally consistent, information is complete, and fully reflects the user's latest state. The pre-processed user feature vector data is input into a pre-trained mapping and matching model. Based on the model's built-in feature-material-strategy three-dimensional association matching rules, a comprehensive similarity comparison, feature fit calculation, and scenario adaptability matching are performed between the user's multi-dimensional feature vector and the features of the advertising materials and the parameters of the delivery strategy. A quantitative matching score is obtained through a combination of vector distance algorithm, feature cross-matching algorithm, and preference association algorithm. Based on a preset score range threshold, the matching results are divided into three gradient-based preliminary matching results: high similarity matching results, medium similarity matching results, and low similarity matching results. This completes the initial intelligent adaptation of users, advertising materials, and delivery strategies. After obtaining the initial matching results at different levels, the system performs refined and structured analysis on various matching results, deeply extracting the global similarity score, the degree of fit of each single feature dimension, the adaptation level of the real-time delivery scenario, and the precise matching tags corresponding to user preferences. This constructs a multi-dimensional, quantifiable, and mappable matching evaluation system, achieving precise correlation and one-to-one mapping between different matching accuracy results and various adjustable parameters for ad delivery, providing a quantitative basis for subsequent differentiated parameter optimization. The system dynamically adjusts the weight of ad delivery based on different levels of matching results. When a high-similarity matching result is determined, the system identifies the current ad delivery weight. The ad creative content, delivery format, and real-time user behavior characteristics, long-term interests, spending habits, and current delivery scenarios are highly compatible, demonstrating excellent user fit and conversion potential. Therefore, the ad's traffic competition weight, front-end ranking display weight, and strategy optimization weight are positively increased across multiple dimensions, enhancing its display priority and traffic allocation share in the overall candidate ad pool. When the matching result is a medium similarity match, it is determined that the ad creative and user characteristics have some dimensions of compatibility and some dimensions of slight deviation, with an overall moderate degree of compatibility. The system maintains the pre-set baseline delivery weight of the ad unchanged, ensuring a stable and balanced traffic allocation level.When the matching result is low similarity, the current ad creative is determined to deviate significantly from the user's actual needs, interests, and scenario, classifying it as a low-fit, low-conversion-potential ad creative. The system then applies a tiered demotion process, gradually reducing its traffic allocation priority and display weight. This ultimately achieves a refined allocation of platform traffic resources, precisely tilting towards highly compatible ads and reasonably limiting the flow of inefficient ads. Simultaneously, the system adaptively and intelligently adjusts ad exposure frequency based on the matching result's fit level. For high-fit, high-quality ad creatives, while strictly adhering to user fatigue thresholds and user experience constraints, the effective exposure frequency within a unit time period is appropriately increased, encrypting high-quality ad creatives. To maximize conversion potential, the user reach density of creative materials is optimized. For moderately matched ad creatives, a stable exposure mechanism with even intervals is adopted to maintain the system's preset baseline exposure frequency, ensuring adequate reach while avoiding resource waste. For low-match ads and unsuitable ads with negative user feedback, the exposure frequency quota is proactively tightened and the time interval between single exposures is lengthened to minimize the waste of system resources and user experience caused by ineffective exposures and inefficient reach. In terms of push time adaptation, precise time-based push is achieved based on the scene and time-based adaptation score in the matching results. For high-match ad tasks, priority is given to targeting users with the highest activity, strongest interaction intention, and highest conversion probability. The system prioritizes targeted, high-density push notifications during peak user activity times, while simultaneously reducing and minimizing the proportion of ad delivery during off-peak hours to improve time-based targeting accuracy. For medium and low-match ad tasks, only a small number of alternative time slots are reserved for trial, lightweight push notifications, completely avoiding inefficient push notification times when users are offline, inactive, or have limited usage scenarios. This achieves a high degree of alignment between push timing and user behavior rhythms. Furthermore, the system adaptively adjusts ad display styles based on user characteristics, preferences, and creative adaptation results. It dynamically switches between various ad styles, such as feed styles, lightweight card styles, immersive full-screen styles, and dynamic short video styles, based on user historical interaction preferences, device status, and usage habits. The system achieves personalized and adaptive display styles for each user, using the same display format. After coordinating and differentiating the four core delivery parameters—ad placement weight, exposure frequency, push time period, and display style—the system performs global coupling verification, logical consistency detection, and parameter conflict correction on all adjusted parameters. It eliminates extreme value anomalies, logically mutually exclusive, and scenario-incompatible parameter configurations. Ultimately, it generates a refined, intelligent, and directly executable target delivery strategy that fully adapts to the user's real-time characteristics, fits the current delivery scenario, balances delivery effectiveness and user experience, and ensures parameter coordination and consistency. This provides reliable strategy support for subsequent precise ad delivery execution and model iteration optimization.

[0027] Step four: Based on the target delivery strategy, synchronize feature data with the delivery strategy through a standardized interface, and use a distributed asynchronous processing architecture to perform lightweight inference optimization on the feature analysis model to obtain the precise delivery execution instructions for advertising in a high-concurrency environment.

[0028] It should be understood that the specific process for obtaining the optimized feature weight allocation and matching rules is as follows: Based on the target delivery strategy, data exchange is achieved through a standardized RPC interface to obtain delivery data synchronized at the millisecond level; Based on millisecond-level synchronous delivery data, a distributed asynchronous processing architecture is used to perform lightweight inference optimization on the feature analysis model to obtain the precise ad delivery execution instructions under high concurrency environment; The specific process for lightweight inference optimization is as follows: Receive the delivery data synchronously obtained through the standardized RPC interface, perform real-time cleaning, field validation, redundancy removal and standardization processing on the delivery data, and construct real-time inference input data; The model inference task is split and load balanced based on a distributed asynchronous processing architecture. Hash sharding is performed based on user identifier and deployment scenario to distribute massive concurrent inference tasks to each distributed computing node. An asynchronous queue mechanism is used to decouple requests and inference computation to avoid blocking latency caused by synchronous serial computation. Perform lightweight inference optimization on the feature analysis model, enable the hot feature result caching and reuse mechanism, reuse historical valid inference results for users and scenarios whose feature status has not fluctuated within a preset short period, and perform new inference calculations for objects with incremental behavior changes and strategy updates. The asynchronous inference results of each distributed node are aggregated, verified, fused, normalized, and latency-compliant. Based on these inference results, an ad delivery execution instruction adapted to a high-concurrency online delivery environment is generated.

[0029] In this embodiment, based on the generated target delivery strategy, a high-speed data communication channel is established between the terminal SDK and the delivery service cluster through a standardized RPC interface preset by the server. Utilizing the low latency, high throughput, and cross-module real-time communication characteristics of the RPC interface, bidirectional synchronous transmission of delivery strategy parameters, real-time user characteristics, material matching information, and scene configuration data is achieved. This avoids the problems of high latency, data asynchrony, and packet loss associated with traditional HTTP interfaces. The system retrieves all runtime data during the strategy delivery process in real time, ultimately achieving millisecond-level real-time synchronization of delivery data. This obtains standardized delivery synchronization data with consistent timing, complete parameters, and real-time status, providing immediate data input for subsequent model inference calculations. After obtaining the millisecond-level synchronized delivery data, the system abandons the inefficient computation mode of traditional single-machine synchronous inference and adopts a highly available, high-throughput distributed asynchronous processing architecture to perform comprehensive lightweight inference optimization on the feature analysis model. This adapts to the online delivery business scenario of massive users, high concurrency requests, and millisecond-level responses in internet advertising, efficiently generating directly executable and precise advertising delivery instructions. The specific implementation steps of the lightweight inference optimization are as follows: The system receives raw delivery data synchronously transmitted via a standardized RPC interface in real time. It immediately performs real-time cleaning, field-by-field validity verification, redundant data removal, and global standardization and normalization of the raw delivery data. This unifies the field formats, data precision, and value ranges of different data sources, filtering out erroneous and redundant data caused by network jitter, duplicate reporting, and interface anomalies. This constructs a clean, standardized, dimensionally consistent, and real-time effective model inference input dataset, ensuring the accuracy and validity of the model inference input data. Subsequently, the system relies on a distributed asynchronous processing architecture to intelligently shard and decompose massive model inference tasks and perform global load balancing scheduling. Using a preset hash sharding algorithm, concurrent inference tasks are evenly sharded based on multiple dimensions such as user unique identifier, ad delivery scenario, material type, and delivery time period. This evenly distributes massive, high-density real-time inference tasks to various distributed computing nodes. Simultaneously, an asynchronous task queue mechanism completely decouples the front-end delivery request from the back-end model inference calculation process, completely eliminating high-concurrency performance problems such as task queuing, thread blocking, and response timeouts caused by traditional synchronous serial computing modes, significantly improving the overall inference throughput and response speed of the system.During the model inference computation phase, the system performs lightweight inference computation on the deployed feature analysis model. This is achieved by dynamically shielding redundant computation branches with low contribution rates within the model, fixing static parameters, and only updating dynamically incremental feature parameters to reduce computational power consumption. Simultaneously, a hot feature inference result caching and reuse mechanism is enabled. For existing users and fixed delivery scenarios where user feature states have not fluctuated significantly within a preset short time period, the delivery scenario remains unchanged, and the delivery strategy remains stable, the system directly retrieves and reuses stored historical valid inference results, eliminating the need to repeatedly execute full model computation. Only for incremental objects with changes in user behavior, feature weight updates, or delivery strategy adjustments, a completely new full model inference computation is initiated, ensuring inference accuracy is maintained. While minimizing computational overhead, the system significantly reduces redundant computing power consumption and compresses inference time. After each distributed computing node independently completes asynchronous lightweight inference operations, the system centrally aggregates, cross-validates, fuses and normalizes multiple results, and verifies inference latency compliance for all distributed inference results. Invalid inference data with timeouts, computational biases, or node anomalies is removed, while accurate, compliant, and low-latency valid inference results are retained. Finally, based on the validated optimal feature inference results, the system automatically assembles and encapsulates standardized instructions containing all execution parameters such as placement weight, exposure frequency, push time period, and display style. This generates batches of precise ad placement execution instructions fully adapted to high-concurrency online placement environments and capable of driving the ad SDK to execute placement actions in real time.

[0030] Step 5: After executing the ad delivery according to the precise ad delivery execution instruction, obtain the ad delivery performance data for the entire life cycle, quantify the impact weight of different feature dimensions on ad conversion efficiency, and input the delivery performance data back into the feature construction model and strategy matching model to obtain the optimized feature weight allocation and matching rules.

[0031] It should be understood that the specific process for obtaining the optimized feature weight allocation and matching rules is as follows: Based on the results of the targeted advertising execution instructions, the entire lifecycle data of ad exposure, clicks, conversions, retention and repeat purchases is collected to obtain quantitative data on the effectiveness of the advertising campaign. Based on quantitative data on advertising performance, the impact weights of different feature dimensions on advertising conversion efficiency are quantified to obtain the quantitative impact weight results. Based on the quantified impact weight results, a multi-dimensional visualization report of the advertising performance is generated. The quantitative data of the advertising performance is then input into the feature construction model and the strategy matching model to obtain the optimized feature weight allocation and matching rules.

[0032] In this embodiment, the ad SDK is driven by the issued ad targeting execution command to complete all ad delivery actions, including precise exposure of the corresponding ad creative, time-based push, frequency control, and personalized style display. Throughout the entire ad delivery process, a full-link, full-cycle, and uninterrupted data collection mechanism is initiated. Relying on front-end tracking, server-side log statistics, and back-end data feedback channels, comprehensive data is collected covering the entire lifecycle of ad delivery, including ad display exposure, effective exposure duration, user click behavior, click-through success rate, page dwell interaction depth, ad-driven conversion behavior, user retention status, secondary visit behavior, and repeat purchase behavior. This data encompasses the entire lifecycle of ad delivery, from pre-delivery to post-delivery performance. Simultaneously, the raw data is processed... Effectiveness screening is performed to remove invalid and interfering data such as bot clicks, accidental clicks, and instant exits. After data cleaning, statistical summarization, and quantitative calculation, objective, accurate, and usable quantitative data on advertising performance for model training and analysis are generated, fully reflecting the actual implementation effect of the current batch of advertising strategies and feature matching schemes. After obtaining standardized quantitative data on advertising performance, the system relies on a preset feature contribution analysis algorithm, using the final conversion efficiency, click-through rate, retention rate, and repurchase rate as core evaluation indicators, to decompose and quantify the actual contribution ratio and influence of user basic feature dimensions, behavioral feature dimensions, interest preference feature dimensions, consumption habit feature dimensions, and advertising scenario feature dimensions in this advertising conversion process. The system uses impact weighting to accurately identify the positive impact of each dimension of features on ad placement and their influence on inefficient placement, low conversion rates, and high negative feedback. This yields quantified impact weights for each dimension, clarifying the differentiated contribution patterns of different user characteristics, scenario characteristics, and preference characteristics to ad placement performance. Based on these quantified impact weights, the system automatically integrates multi-dimensional placement metrics data through a backend data visualization component. This generates multi-dimensional visualization reports on placement performance, including feature matching effectiveness, dimension contribution percentage, user conversion trends, scenario placement revenue, and strategy adaptation effectiveness. This allows operations personnel to intuitively grasp the overall status of placement and feature adaptation, while simultaneously displaying validated and standardized ad placement results. Quantitative data, serving as real sample data, is continuously fed back into the feature building model and strategy matching model at the front end to correct the original parameter deviations of the models. The models continuously iterate the optimal weight allocation ratio of each dimension feature based on real campaign performance sample data, dynamically correcting the problems of solidified static weights, imbalanced dimension contributions, and insufficient generalization ability of matching rules. At the same time, the models continuously optimize the correlation matching logic and adaptation rule thresholds between features, creatives, and strategies, eliminating inefficient matching logic and strengthening high-conversion matching correlations. Finally, the optimized feature weight allocation scheme and strategy matching rules are output, which are adapted to the characteristics of the current user group, fit the real-time campaign environment, highly match user conversion patterns, and can continuously improve the accuracy of ad campaigns.

[0033] like Figure 3The diagram shows the structure of the advertising SDK delivery system based on multi-dimensional user profiles provided in this application embodiment. It includes: a data acquisition module, a feature construction module, a dynamic update module, a strategy matching module, and a reverse optimization module. The data acquisition module collects user basic information features, user behavior features, user interest and preference features, user consumption habit features, and delivery scenario features to obtain full multi-dimensional user feature data. It then performs preprocessing operations such as cleaning, deduplication, outlier removal, and normalization, and performs weighted fusion calculations on the features of each dimension to obtain a multi-dimensional user panoramic feature set. The feature construction module, based on the multi-dimensional user panoramic feature set, captures all user-advertisement interaction behaviors in real time and incrementally collects user behavior change data. When user behavior change data triggers a preset update threshold, it adjusts the weight allocation of each dimension feature and the feature fusion result to obtain the full multi-dimensional user profile feature set. The system comprises: a multi-dimensional user panoramic feature set updated in real time; a dynamic update module, which matches the most suitable ad content using a mapping matching model based on the real-time updated multi-dimensional user panoramic feature set, and simultaneously adjusts ad placement weights, exposure frequency, push time periods, and display styles to obtain the target placement strategy; a strategy matching module, which synchronizes feature data with the placement strategy through a standardized interface, and uses a distributed asynchronous processing architecture to perform lightweight inference optimization on the feature analysis model to obtain the ad placement execution instructions under high concurrency; and a reverse optimization module, which obtains the full-cycle ad placement effect data after the ad placement is executed according to the ad placement execution instructions, quantifies the impact weight of different feature dimensions on ad conversion efficiency, and inputs the placement effect data back into the feature construction model and the strategy matching model to obtain the optimized feature weight allocation and matching rules.

[0034] In this embodiment, a modular, decoupled, and iterative architecture is adopted, comprising five core modules: data acquisition, feature construction, dynamic updating, strategy matching, and reverse optimization. This forms a complete closed loop from feature acquisition, profile iteration, strategy generation, high-concurrency deployment, to performance feedback. The data acquisition module comprehensively acquires raw data across five dimensions—basic user information, behavior, interests, consumption habits, and deployment scenarios—through SDK tracking, device parameter reading, and behavior log capture. It then performs preprocessing such as cleaning, deduplication, anomaly removal, and normalization, followed by differentiated weighted fusion calculations to construct a unified, multi-dimensional user panoramic feature set. The feature construction module uses this set as a historical benchmark, continuously capturing all user interactions with advertisements through a built-in real-time monitoring component. It quantifies the magnitude of behavioral changes and automatically initiates dynamic adjustment of feature weights when preset thresholds are triggered, updating the user panoramic profile in real time and eliminating lag bias. The dynamic update module extracts feature vectors based on the latest user profiles and performs hierarchical similarity matching through a mapping matching model and feature-material-strategy rules. Based on high, medium, and low matching results, it synchronously and collaboratively adjusts ad placement weights, exposure frequency, push times, and display styles to generate refined target placement strategies. The strategy matching module achieves millisecond-level synchronization of strategy data via a standardized RPC interface and employs a distributed asynchronous processing architecture to perform lightweight inference optimization on the feature analysis model (including task sharding, load balancing, asynchronous queues, model pruning, and hotspot cache reuse), ensuring stable output of accurate ad placement execution commands under high-concurrency scenarios. The reverse optimization module collects full-cycle performance data, including exposure, clicks, conversions, retention, and repeat purchases, quantifies the impact weight of each feature dimension on conversion efficiency, and continuously inputs performance data back into the feature construction and strategy matching model, iteratively optimizing feature weight allocation and matching rules. The overall system achieves technological upgrades from static, fixed user profiles to dynamic, real-time iteration; from experience-driven to data-driven adaptive collaboration in placement strategies; and from one-time setting of model rules to continuous closed-loop self-evolution, improving the accuracy, real-time performance, and stability of ad placement.

[0035] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope and intent of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and variations.

Claims

1. An advertising SDK delivery method based on multi-dimensional user profiles, characterized in that, Includes the following steps: Step 1: Collect user basic information features, user behavior features, user interest and preference features, user consumption habit features, and advertising scenario features to obtain full data of multi-dimensional user features. Perform normalization preprocessing operation and perform weighted fusion calculation on the features of each dimension to obtain a multi-dimensional user panoramic feature set. Step 2: Based on the multi-dimensional user panoramic feature set, capture all user interaction behaviors with advertisements in real time and incrementally collect user behavior change data. When the user behavior change data triggers a preset update threshold, adjust the weight allocation of each dimension feature and the feature fusion result to obtain the real-time updated multi-dimensional user panoramic feature set. Step 3: Based on the real-time updated multi-dimensional user panoramic feature set, match the most suitable advertising content through the mapping matching model, and simultaneously adjust the advertising placement weight, exposure frequency, push time period and display style to obtain the target placement strategy; Step 4: Based on the target delivery strategy, synchronize feature data with the delivery strategy through a standardized interface, and use a distributed asynchronous processing architecture to perform lightweight inference optimization on the feature analysis model to obtain the execution instructions for accurate ad delivery in a high-concurrency environment. Step 5: After executing the ad delivery according to the precise ad delivery execution instruction, obtain the ad delivery performance data for the entire life cycle, quantify the impact weight of different feature dimensions on ad conversion efficiency, and input the delivery performance data back into the feature construction model and strategy matching model to obtain the optimized feature weight allocation and matching rules.

2. The advertising SDK delivery method based on multi-dimensional user profiles as described in claim 1, characterized in that: The specific process for obtaining the multi-dimensional user panoramic feature set is as follows: Based on the user terminal's operating status, data such as age, gender, region, device model, system version, network environment, and terminal type are collected to obtain basic user information feature data. Based on the operation records of the user terminal during operation, collect advertising interaction records, click trajectories, single ad dwell time, ad revisit frequency, and page jump paths within the APP to obtain user behavior feature data; Based on the interaction records in the user behavior feature data, the types of advertisements with high-frequency interactions, content focus, click preferences, and negative feedback behaviors are collected to obtain user interest preference feature data. Based on historical transaction and conversion records, we collect data on consumption levels, historical advertising conversion records, potential consumption tendencies, and payment frequency to obtain user consumption habit characteristics data. Based on the current advertising environment, data on advertising time periods, user app usage scenarios, terminal screen orientation (portrait or landscape), and network environment are collected to obtain advertising scenario characteristic data.

3. The advertising SDK delivery method based on multi-dimensional user profiles as described in claim 2, characterized in that: The obtained multi-dimensional user panoramic feature set also includes: Based on the user basic information feature data, user behavior feature data, user interest and preference feature data, user consumption habit feature data, and advertising scenario feature data, the data is aggregated to obtain the full data of the multi-dimensional user features. Based on the full dataset of multi-dimensional user features, preprocessing operations such as cleaning, deduplication, outlier removal, and normalization are performed to filter out invalid and dirty data, resulting in preprocessed feature data. Based on the preprocessed feature data, extract the numerical vectors of each dimension of features, and perform weighted fusion calculation on the numerical vectors of each dimension of features to obtain the weight allocation results of each dimension of features. Based on the weight allocation results, the weighted and fused feature vectors of each dimension are concatenated and combined to obtain the multi-dimensional feature vector set of the current user. The multidimensional user panoramic feature set is obtained by structurally encapsulating the multidimensional feature vector set.

4. The advertising SDK delivery method based on multi-dimensional user profiles as described in claim 1, characterized in that: The specific process for adjusting the weight allocation of features in each dimension and the feature fusion result is as follows: Based on the multi-dimensional user panoramic feature set, the SDK's built-in listening component captures all user interaction behaviors with advertisements in real time, obtaining incremental user behavior change data. Based on the incremental user behavior change data, the magnitude of behavior change is calculated, and it is determined whether the magnitude of behavior change triggers a preset update threshold. The magnitude of behavior change is used to characterize the degree of deviation between the user's real-time incremental behavior and the historical baseline behavior in the advertising interaction scenario. Based on the judgment result of triggering a preset update threshold according to the magnitude of the behavioral change, the feature update mechanism is automatically activated to adjust the weight allocation of the corresponding dimension features and the feature fusion result, specifically: If the magnitude of the behavioral change is greater than or equal to the preset update threshold, it is determined to be a trigger condition for updating. The user's current behavior deviates from the historical profile features, and the original feature weight allocation and feature fusion results cannot accurately match the user's latest status. If the change in behavior is less than the preset update threshold, it is determined that the update condition has not been triggered, the user's advertising interaction behavior and interest preferences are normal, and the original weights and fusion results remain unchanged.

5. The advertising SDK delivery method based on multi-dimensional user profiles as described in claim 4, characterized in that: The specific process of the automatic feature update mechanism is as follows: Based on the preset behavioral dimension weight mapping rules and the quantified behavioral change magnitude, the weights of user behavior features, user interest and preference features, user consumption habits, and advertising scenario features are dynamically adjusted using gradients. Specifically: The weights of the behavioral features corresponding to the user's new positive interaction behavior are positively gained and adjusted. The magnitude of the current behavior change is input into the behavior dimension weight mapping rule, and the weights of the user's behavior features after gain are output. The weights of emerging user interest and preference features are positively adjusted by inputting the magnitude of the current behavioral change into the behavioral dimension weight mapping rule, and the weights of the user interest and preference features after gain are output. The consumption habit features corresponding to the user's current incremental consumption behavior are positively adjusted, the magnitude of the current behavior change is input into the behavior dimension weight mapping rule, and the weight of the user's consumption habit features after the gain is output. For delivery scenario features with high behavioral change amplitude, positive gain adjustment is performed. The current behavioral change amplitude is input into the behavioral dimension weight mapping rule, and the weight of the delivery scenario feature after gain is output. Based on the adjusted weights of user behavior features, user interest and preference features, user consumption habits, and advertising scenarios, normalization is performed to generate a panoramic user feature result that adapts to the latest user behavior status and fits the real-time advertising scenario.

6. The advertising SDK delivery method based on multi-dimensional user profiles as described in claim 1, characterized in that: The specific process for obtaining the target delivery strategy is as follows: Based on the real-time updated multi-dimensional user panoramic feature set, extract the multi-dimensional feature vector of the current user to obtain the current user feature vector data; Based on the current user feature vector data, similarity calculation and matching are performed in the mapping matching model through preset feature-material-strategy association rules to obtain preliminary matching results. The preliminary matching results include high similarity matching results, medium similarity matching results, and low similarity matching results. Based on the preliminary matching results, the ad placement weight, exposure frequency, push time period, and display style will be adjusted synchronously, specifically as follows: The preliminary matching results are structured and analyzed to extract similarity scores between user features and advertising materials and delivery strategies, matching fit of features in each dimension, scene adaptability level and preference matching tags, and to build a quantitative matching evaluation system to achieve accurate correlation mapping between matching results and delivery parameters. Based on the similarity level of the initial matching results, the advertising placement weight is dynamically adjusted. If the matching result is high similarity, it is determined that the current advertising material is highly compatible with user behavior characteristics, interests, consumption habits and placement scenarios. The traffic competition weight, ranking display weight and strategy optimization weight of the advertisement are positively increased to improve the priority of the advertisement in the candidate placement pool.

7. The advertising SDK delivery method based on multi-dimensional user profiles as described in claim 6, characterized in that: The obtained target delivery strategy also includes: If the matching result is of medium similarity, the basic advertising weight is maintained. If the matching result is of low similarity, the advertising weight is reduced in a gradient manner to lower the traffic allocation priority of unsuitable ads and achieve precise allocation of traffic resources. The frequency of ad exposure is adaptively adjusted based on the initial matching results, specifically as follows: For high-match ads, increase the effective exposure frequency per unit time and strengthen the user reach density of high-quality, well-matched creatives; for medium-match ads, adopt a uniform interval exposure mode to maintain the baseline exposure frequency; for low-match ads and ads with negative match characteristics, tighten the exposure frequency limit, lengthen the exposure interval, and reduce ineffective and inefficient exposures. If the result is a high match, prioritize targeted push during the most active time period and reduce the proportion of push during non-core time periods; if the result is a medium or low match, only retain alternative time periods for trial push, avoiding inefficient time periods when users are silent or inactive. After completing the synchronous and collaborative optimization of the placement weight, exposure frequency, push time period, and display style, all parameters are coupled and verified to generate a target placement strategy that adapts to the real-time characteristics of users and fits the placement scenario.

8. The advertising SDK delivery method based on multi-dimensional user profiles as described in claim 1, characterized in that: The specific process for obtaining the optimized feature weight allocation and matching rules is as follows: Based on the target delivery strategy, data exchange is achieved through a standardized RPC interface to obtain delivery data synchronized at the millisecond level; Based on the millisecond-level synchronous delivery data, a distributed asynchronous processing architecture is used to perform lightweight inference optimization on the feature analysis model to obtain the precise delivery execution instructions for advertising in a high-concurrency environment. The specific process for performing lightweight inference optimization is as follows: Receive the delivery data synchronously obtained through the standardized RPC interface, perform real-time cleaning, field validation, redundancy removal and standardization processing on the delivery data, and construct real-time inference input data; The model inference task is split and load balanced based on a distributed asynchronous processing architecture. Hash sharding is performed based on user identifier and deployment scenario to distribute massive concurrent inference tasks to each distributed computing node. An asynchronous queue mechanism is used to decouple requests and inference computation to avoid blocking latency caused by synchronous serial computation. Perform lightweight inference optimization on the feature analysis model, enable the hot feature result caching and reuse mechanism, reuse historical valid inference results for users and scenarios whose feature status has not fluctuated within a preset short period, and perform new inference calculations for objects with incremental behavior changes and strategy updates. The asynchronous inference results of each distributed node are aggregated, verified, fused, normalized, and latency-compliant. Based on these inference results, an ad delivery execution instruction adapted to a high-concurrency online delivery environment is generated.

9. The advertising SDK delivery method based on multi-dimensional user profiles as described in claim 1, characterized in that: The specific process for obtaining the optimized feature weight allocation and matching rules is as follows: Based on the results of the targeted advertising execution instructions, the entire lifecycle data of ad exposure, clicks, conversions, retention, and repeat purchases is collected to obtain quantitative data on the advertising performance. Based on the quantitative data of advertising performance, the influence weight of different feature dimensions on advertising conversion efficiency is quantified to obtain the quantitative influence weight results. Based on the quantified impact weight results, a multi-dimensional visualization report of the advertising performance is generated. The quantified data of the advertising performance is then input into the feature construction model and the strategy matching model to obtain the optimized feature weight allocation and matching rules.

10. A system applying the advertising SDK delivery method based on multi-dimensional user profiles as described in any one of claims 1-9, characterized in that, include: The module includes a data acquisition module, a feature construction module, a dynamic update module, a strategy matching module, and a reverse optimization module. The data acquisition module is used to collect user basic information features, user behavior features, user interest and preference features, user consumption habit features and advertising scenario features to obtain full data of multi-dimensional user features. It also performs cleaning, deduplication, outlier removal and normalization preprocessing operations, and performs weighted fusion calculation on the features of each dimension to obtain a multi-dimensional user panoramic feature set. The feature construction module is used to capture the full interaction behavior of users and advertisements in real time and incrementally collect user behavior change data based on the multi-dimensional user panoramic feature set. When the user behavior change data triggers a preset update threshold, the weight allocation of each dimension feature and the feature fusion result are adjusted to obtain the real-time updated multi-dimensional user panoramic feature set. The dynamic update module is used to match the most suitable advertising content through a mapping matching model based on the real-time updated multi-dimensional user panoramic feature set, and simultaneously adjust the advertising placement weight, exposure frequency, push time period and display style to obtain the target placement strategy. The strategy matching module is used to synchronize feature data with the target delivery strategy through a standardized interface, and to perform lightweight inference optimization of the feature analysis model using a distributed asynchronous processing architecture to obtain the precise ad delivery execution instructions in a high-concurrency environment. The reverse optimization module is used to obtain full-cycle advertising performance data after executing the advertising precise delivery execution instruction, quantify the influence weight of different feature dimensions on advertising conversion efficiency, and input the delivery performance data back into the feature construction model and strategy matching model to obtain the optimized feature weight allocation and matching rules.

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