Large factory cross-platform user drainage and growth promotion system based on artificial intelligence

Abstract

The invention discloses a large factory cross-platform user drainage and growth promotion system based on artificial intelligence, and relates to the technical field of monitoring analysis, and the system comprises a data collection and processing module which is used for obtaining user cross-platform interaction behavior data, and generating a multi-platform user behavior feature vector set; the data mining module is used for performing cross-platform user identity identification code matching processing on the multi-platform user behavior feature vector set to generate a user cross-platform behavior feature map; the promotion strategy generation module is used for constructing a dynamic diversion strategy generation matrix according to the user cross-platform behavior characteristic spectrum and generating a user cross-platform diversion promotion strategy; and the optimization module is used for acquiring the real-time user feedback data flow, constructing a flow guide strategy dynamic correction model based on the real-time user feedback data flow, optimizing the cross-platform flow guide promotion strategy of the user, and deploying the cross-platform flow guide promotion strategy to a target platform to execute user growth operation.

Description

Technical Field

[0001] The present application relates to the field of monitoring and analysis technology, and in particular to a large-scale cross-platform user traffic diversion and growth promotion system based on artificial intelligence. Background Art

[0002] With the booming digital commerce landscape, it's increasingly common for large companies to operate across multiple platforms, making cross-platform user traffic generation and growth promotion a critical requirement. Today, diverse platforms, such as social media, e-commerce, and information, aggregate vast amounts of user data, encompassing a wealth of information on users' interests, preferences, and behavioral habits. The continuous evolution of artificial intelligence (AI) technology is making it possible to effectively leverage this user data for targeted traffic generation and promotion. AI algorithms and models enable deep mining of cross-platform user data. For example, through machine learning, user behavior and interactions across platforms can be analyzed to create precise user profiles, enabling the development of more targeted traffic generation and promotion strategies. AI can also monitor user activity in real time, enabling timely adjustments to promotional strategies to adapt to evolving market conditions and user needs.

[0003] In related technologies, traditional large-scale cross-platform user traffic and growth promotion systems find it difficult to effectively integrate heterogeneous behavioral data of users on multiple platforms such as social, e-commerce, and content, resulting in fragmented user portraits. They are also unable to capture the patterns of user interest migration across platforms in real time, causing strategy lags, which in turn leads to reduced efficiency in user traffic and growth, resulting in waste of promotion resources, and there is room for improvement. Summary of the Invention

[0004] In response to the shortcomings of existing technologies, this application provides an artificial intelligence-based cross-platform user traffic and growth promotion system for large companies.

[0005] First, this application provides an AI-based cross-platform user traffic generation and growth promotion system for large companies, including: A data collection and processing module is used to obtain user cross-platform interaction behavior data, input the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling, and generate a multi-platform user behavior feature vector set; A data mining module is used to perform cross-platform user identity code matching processing on the multi-platform user behavior feature vector set, and establish a unified cross-platform identity profile of the user based on the processing result, and then conduct platform behavior preference mining on the target user group based on the cross-platform unified identity profile to generate a cross-platform user behavior feature map; A promotion strategy generation module is used to construct a dynamic traffic diversion strategy generation matrix based on the user cross-platform behavior feature map, calculate the expected user retention growth value of each traffic diversion strategy through a preset cross-platform benefit estimation model, and then generate a user cross-platform traffic diversion promotion strategy based on the expected user retention growth value of each traffic diversion strategy; The optimization module is used to obtain real-time user feedback data streams, and build a dynamic correction model for traffic diversion strategies based on the real-time user feedback data streams, optimize the user cross-platform traffic diversion and promotion strategies, and deploy the optimized cross-platform traffic diversion and promotion strategies to the target platform to perform user growth operations.

[0006] Preferably, the user cross-platform interaction behavior data is input into a behavior feature deep extraction network to perform cross-platform feature association modeling to generate a multi-platform user behavior feature vector set, specifically including: Establish a cross-platform data collection channel, and based on the cross-platform data collection channel, collect user cross-platform interaction behavior data corresponding to multiple platforms through encrypted tracking technology. The user cross-platform interaction behavior data includes real-time click trajectory data, page dwell time data, and cross-application jump path data; Aggregate the collected user cross-platform interaction behavior data, and then generate a user interaction behavior feature set based on the aggregation processing results, wherein the user interaction behavior feature set includes user device fingerprint features, cross-platform account association features, and behavior time series parameter features; Constructing a behavior feature deep extraction network, performing multi-dimensional feature calculation on the user interaction behavior feature set based on the behavior feature deep extraction network, and then generating a primary feature set; A cross-platform feature association model is performed on the primary feature set, and the weight distribution corresponding to each platform is calculated, and then the weight distribution corresponding to each platform is integrated to obtain a multi-platform user behavior feature vector set.

[0007] Preferably, the multi-platform user behavior feature vector set is subjected to cross-platform user identity identification code matching processing, and a cross-platform unified identity portrait of the user is established based on the processing result. Then, the platform behavior preference of the target user group is mined based on the cross-platform unified identity portrait to generate a cross-platform user behavior feature map, which specifically includes: Build a user identity matching decision set based on the user's device fingerprint features and cross-platform account association features, perform feature migration on the user's cross-platform interaction behavior data corresponding to multiple platforms, generate a user authentication feature set, and then match the user identity matching decision set with the user authentication feature set to establish a unified cross-platform identity profile for the user; Construct a dynamic preference prediction model, extract spatiotemporal features of the user's unified identity portrait across platforms, and generate a behavioral preference prediction map; The user's unified cross-platform identity portrait and behavioral preference prediction map are projected into three-dimensional space and time to generate a user cross-platform behavioral feature map.

[0008] Preferably, a dynamic traffic diversion strategy generation matrix is constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic diversion strategy is calculated through a preset cross-platform benefit estimation model. Then, based on the expected user retention growth value of each traffic diversion strategy, a user cross-platform traffic diversion promotion strategy is generated, specifically including: Based on the user's cross-platform behavior feature map, a dynamic traffic diversion strategy generation matrix is constructed, and then an initial traffic diversion strategy candidate set is generated based on the dynamic traffic diversion strategy generation matrix; The expected user retention growth value of each candidate strategy in the initial traffic diversion strategy candidate set is calculated through the preset cross-platform benefit estimation model, the calculation results are screened, and then a user cross-platform traffic diversion promotion strategy is generated based on the screening results.

[0009] Preferably, a real-time user feedback data stream is obtained, and a dynamic correction model for traffic diversion strategy is constructed based on the real-time user feedback data stream, the user cross-platform traffic diversion and promotion strategy is optimized, and the optimized cross-platform traffic diversion and promotion strategy is deployed to the target platform to perform user growth operations, specifically including: Acquire real-time user feedback data streams through a real-time data collection gateway to build a dynamic monitoring indicator system. The real-time user feedback data streams include behavioral response delay distribution data, conversion funnel parameter data, and sentiment tendency index data. Based on the dynamic monitoring indicator system, abnormal pattern recognition is performed on the real-time user feedback data stream to generate strategy failure warning signals and preference deviation detection reports; Based on the strategy failure warning signal and preference drift detection report, a dynamic correction model for the traffic diversion strategy is constructed, and based on the dynamic correction model for the traffic diversion strategy, the user cross-platform traffic diversion and promotion strategy is optimized to generate an optimized traffic diversion and promotion strategy, and the optimized traffic diversion and promotion strategy is integrated with the user cross-platform traffic diversion and promotion strategy to generate a final cross-platform traffic diversion and promotion strategy, and the final cross-platform traffic diversion and promotion strategy is synchronized to the target platform through a distributed deployment mechanism to execute user growth operations.

[0010] Preferably, the process of constructing the dynamic correction model of the drainage strategy specifically includes: Generate a behavioral response correction factor based on the behavioral response delay distribution data in the real-time user feedback data stream, and build a user preference shift detection model based on the conversion funnel parameter data and sentiment index data. Based on the user preference shift detection model, classify and identify abnormal patterns in the real-time user feedback data stream, generate a strategy failure warning signal and a preference shift detection report, and confirm the preference shift correction matrix based on the preference shift detection report. A policy gradient update module is designed to define the policy optimization objective based on the reward function, and then generate the gradient update rule. Then, a dynamic correction model of the drainage strategy is generated by integrating the behavior response correction factor, the preference offset correction matrix and the gradient update rule.

[0011] Secondly, this application provides an AI-based cross-platform user traffic diversion and growth promotion method for large companies, including the following steps: Obtaining user cross-platform interaction behavior data, inputting the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling, and generating a multi-platform user behavior feature vector set; Performing cross-platform user identity code matching processing on the multi-platform user behavior feature vector set, and establishing a unified cross-platform identity profile of the user based on the processing result, and then conducting platform behavior preference mining on the target user group based on the cross-platform unified identity profile to generate a cross-platform user behavior feature map; A dynamic traffic diversion strategy generation matrix is constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic diversion strategy is calculated through a preset cross-platform benefit estimation model, and then a user cross-platform traffic diversion promotion strategy is generated based on the expected user retention growth value of each traffic diversion strategy; Acquire real-time user feedback data streams, and build a dynamic correction model for traffic diversion strategies based on the real-time user feedback data streams, optimize the user cross-platform traffic diversion and promotion strategies, and deploy the optimized cross-platform traffic diversion and promotion strategies to the target platform to perform user growth operations.

[0012] In a third aspect, the present application provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes any one of the above-mentioned artificial intelligence-based cross-platform user traffic diversion and growth promotion systems for large companies.

[0013] In summary, this application has the following beneficial technical effects: The present application provides a large-scale cross-platform user traffic diversion and growth promotion system based on artificial intelligence. By acquiring user cross-platform interaction behavior data, a multi-platform user behavior feature vector set is generated, and cross-platform user identity identification code matching processing is performed on the multi-platform user behavior feature vector set to generate a user cross-platform behavior feature map, thereby effectively analyzing the user cross-platform interaction behavior data, thereby reducing the occurrence of user portrait fragmentation, and constructing a dynamic traffic diversion strategy generation matrix based on the user cross-platform behavior feature map. The expected user retention growth value of each traffic diversion strategy is calculated through a preset cross-platform benefit estimation model, and a user cross-platform traffic diversion promotion strategy is generated based on the expected user retention growth value of each traffic diversion strategy. Real-time user feedback data streams are obtained, and a dynamic correction model for the traffic diversion strategy is constructed based on the real-time user feedback data streams. The user cross-platform traffic diversion promotion strategy is optimized and deployed to the target platform to perform user growth operations, thereby effectively reducing the occurrence of strategy lag caused by the inability to capture the user's cross-platform interest migration rules in real time, thereby effectively improving the efficiency of user traffic diversion and growth, and effectively reducing the waste of promotion resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 It is a system diagram of an embodiment of the present application.

[0016] Figure 2 It is a flow chart of the method of an embodiment of the present application. DETAILED DESCRIPTION Example 1

[0017] The embodiments of the present application disclose an artificial intelligence-based cross-platform user traffic diversion and growth promotion system for large companies.

[0018] Reference Figure 1 , an AI-based cross-platform user traffic and growth promotion system for large companies, including: A data collection and processing module is used to obtain user cross-platform interaction behavior data, input the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling, and generate a multi-platform user behavior feature vector set; A data mining module is used to perform cross-platform user identity code matching processing on the multi-platform user behavior feature vector set, and establish a unified cross-platform identity profile of the user based on the processing result, and then conduct platform behavior preference mining on the target user group based on the cross-platform unified identity profile to generate a cross-platform user behavior feature map; A promotion strategy generation module is used to construct a dynamic traffic diversion strategy generation matrix based on the user cross-platform behavior feature map, calculate the expected user retention growth value of each traffic diversion strategy through a preset cross-platform benefit estimation model, and then generate a user cross-platform traffic diversion promotion strategy based on the expected user retention growth value of each traffic diversion strategy; The optimization module is used to obtain real-time user feedback data streams, and build a dynamic correction model for traffic diversion strategies based on the real-time user feedback data streams, optimize the user cross-platform traffic diversion and promotion strategies, and deploy the optimized cross-platform traffic diversion and promotion strategies to the target platform to perform user growth operations.

[0019] It should be noted that the user cross-platform interaction behavior data is input into the behavior feature deep extraction network to perform cross-platform feature association modeling to generate a multi-platform user behavior feature vector set, specifically including: Establish a cross-platform data collection channel, and based on the cross-platform data collection channel, collect user cross-platform interaction behavior data corresponding to multiple platforms through encrypted tracking technology. The user cross-platform interaction behavior data includes real-time click trajectory data, page dwell time data, and cross-application jump path data; Aggregate the collected user cross-platform interaction behavior data, and then generate a user interaction behavior feature set based on the aggregation processing results, wherein the user interaction behavior feature set includes user device fingerprint features, cross-platform account association features, and behavior time series parameter features; Constructing a behavior feature deep extraction network, performing multi-dimensional feature calculation on the user interaction behavior feature set based on the behavior feature deep extraction network, and then generating a primary feature set; A cross-platform feature association model is performed on the primary feature set, and the weight distribution corresponding to each platform is calculated, and then the weight distribution corresponding to each platform is integrated to obtain a multi-platform user behavior feature vector set.

[0020] Specifically, a cross-platform data collection channel is established, and based on the cross-platform data collection channel, the user cross-platform interaction behavior data corresponding to multiple platforms is collected through the encrypted embedding technology, wherein the user cross-platform interaction behavior data includes real-time click trajectory data, page dwell time data and cross-application jump path data, and a connection channel with multiple platforms is established. For example, e-commerce platforms, social platforms and information platforms are connected, and data collection points are set up in the pages and applications of each platform through the encrypted embedding technology. When a user clicks on a product link on an e-commerce platform, the encrypted embedding point will record the real-time click trajectory data, including the location and time of the click; when a user browses a page on a social platform, the page dwell time data will be recorded; when a user jumps from an information platform to an e-commerce platform, the cross-application jump path data will be recorded; Secondly, the collected user cross-platform interactive behavior data is aggregated, and then a user interactive behavior feature set is generated based on the results of the aggregation processing. The user interactive behavior feature set includes user device fingerprint features, cross-platform account association features, and behavioral timing parameter features. The collected user cross-platform interactive behavior data is aggregated. For example, the click trajectory data, page dwell time data, and cross-application jump path data of the same user on different platforms are integrated. By analyzing the above data, the user device fingerprint features are extracted, and a unique device fingerprint is generated based on the hardware information, software configuration, etc. of the user device to identify the user's device. At the same time, cross-platform account association features are established. Through the user's registration information, login information, etc. on different platforms, the user's accounts on multiple platforms are associated to understand the user's behavioral associations on different platforms. In addition, the behavioral timing parameter features, such as the user's operation time sequence and operation frequency on different platforms, are analyzed to generate a user interactive behavior feature set to comprehensively reflect the user's behavior patterns on multiple platforms. Then, a behavior feature deep extraction network is constructed. Based on the behavior feature deep extraction network, multi-dimensional feature calculations are performed on the user interaction behavior feature set to generate a primary feature set. Deep learning technology is used to construct a behavior feature deep extraction network, such as a convolutional neural network or a recurrent neural network. The user interaction behavior feature set is input into the behavior feature deep extraction network to perform multi-dimensional feature calculations. For example, in a convolutional neural network, local features and global features in the user interaction behavior feature set are extracted through convolutional layers and pooling layers; in a recurrent neural network, the time series pattern of user behavior is learned based on the behavior temporal parameter characteristics, and a primary feature set is generated through multi-dimensional feature calculations. Finally, a cross-platform feature association model is performed on the primary feature set, and the weight distribution corresponding to each platform is calculated. The weight distribution corresponding to each platform is then integrated to obtain a multi-platform user behavior feature vector set. Based on the primary feature set, a cross-platform feature association model is established. For example, the correlation between the user's behavioral characteristics on the e-commerce platform and the behavioral characteristics of the social platform and the information platform is analyzed. By calculating the weight distribution corresponding to each platform, the degree of influence of the behavioral characteristics of different platforms on the overall user behavior is determined. For example, in some cases, the user's purchasing behavior on the e-commerce platform may be related to the recommended information on the social platform. By calculating the weight distribution, the weight of the influence of the social platform on the e-commerce platform behavior is determined. The weight distribution corresponding to each platform is integrated, and the features in the primary feature set are integrated according to the weight to generate a multi-platform user behavior feature vector set. The multi-platform user behavior feature vector set comprehensively reflects the user's behavioral characteristics on multiple platforms and their mutual relationships, providing rich data support for subsequent user behavior analysis and application.

[0021] It should be noted that the multi-platform user behavior feature vector set is subjected to cross-platform user identity identification code matching processing, and a unified cross-platform identity profile of the user is established based on the processing result. Then, based on the cross-platform unified identity profile, the platform behavior preference of the target user group is mined to generate a cross-platform user behavior feature map, which specifically includes: Build a user identity matching decision set based on the user's device fingerprint features and cross-platform account association features, perform feature migration on the user's cross-platform interaction behavior data corresponding to multiple platforms, generate a user authentication feature set, and then match the user identity matching decision set with the user authentication feature set to establish a unified cross-platform identity profile for the user; Construct a dynamic preference prediction model, extract spatiotemporal features of the user's unified identity portrait across platforms, and generate a behavioral preference prediction map; The user's unified cross-platform identity portrait and behavioral preference prediction map are projected into three-dimensional space and time to generate a user cross-platform behavioral feature map.

[0022] Specifically, a user identity matching decision set is constructed based on the user device fingerprint features and cross-platform account association features, and the feature migration of the user's cross-platform interaction behavior data corresponding to multiple platforms is performed to generate a user identity authentication feature set, and then the user identity matching decision set is matched with the user identity authentication feature set to establish a unified cross-platform identity portrait of the user. The user device fingerprint features contain the hardware information of the user device, such as device model, operating system version, sensor data, etc., which can accurately identify the user device. The cross-platform account association features associate the user's accounts on multiple platforms through the user's registration information, login information and behavior data on different platforms. For example, the mobile phone number used by the user on the e-commerce platform is bound to the account on the social platform. Through this association, the user's behavior data on different platforms can be integrated. Based on the above features, a user identity matching decision set is constructed, matching rules and strategies are formulated, and feature migration is performed on the user's interactive behavior data on multiple platforms. The behavioral characteristics of different platforms are integrated and converted. For example, the user's browsing behavior characteristics on the information platform are correlated with the purchasing behavior characteristics on the e-commerce platform to generate a user authentication feature set. The user identity matching decision set is matched with the user authentication feature set. Based on the matching results, a unified cross-platform identity profile of the user is established. The unified cross-platform identity profile of the user includes the user's basic information, device information, behavioral patterns on multiple platforms, etc., and comprehensively reflects the user's identity characteristics on different platforms. Secondly, a dynamic preference prediction model is constructed to extract spatiotemporal features of the user's unified identity portrait across platforms and generate a behavioral preference prediction map. The dynamic preference prediction model uses deep learning algorithms, such as recurrent neural networks or long short-term memory networks. The above algorithms can process time series data, learn the dynamic changes of user behavior, extract spatiotemporal features of the user's unified identity portrait across platforms, and analyze the user's behavior patterns at different times and spaces. For example, the behavior of users varies in different time periods and geographical locations. By analyzing data such as the user's purchase time on e-commerce platforms, the active time on social platforms, and the browsing time on information platforms, the user's time features are extracted; by analyzing the user's behavior data in different regions, spatial features are extracted. Based on the above spatiotemporal features, the dynamic preference prediction model generates a behavioral preference prediction map. The behavioral preference prediction map shows the user's behavioral preferences at different times and spaces, such as the user's preference for certain products in a specific time period, and the attention paid to different information in different regions. The user's cross-platform unified identity portrait and behavioral preference prediction map are projected in three-dimensional space and time to generate a user cross-platform behavioral feature map. The three-dimensional space and time projection is to display the user's identity information and behavioral preference information in three-dimensional space, with time as one dimension and spatial position as the other two dimensions. The information in the user's cross-platform unified identity portrait and the information in the behavioral preference prediction map are integrated. For example, the user's behavioral preferences at different times and spaces are combined with the user's identity characteristics, and the user's behavioral characteristics on multiple platforms are displayed in three-dimensional space. In this way, a user cross-platform behavioral feature map is generated. The user's cross-platform behavioral feature map intuitively displays the user's behavioral patterns, preferences and associations with identity characteristics on multiple platforms. For example, the user's cross-platform behavioral feature map can show the frequency and behavioral preferences of users using different platforms at specific times and in specific regions, providing more accurate user behavior analysis and helping to optimize services and recommendation systems.

[0023] It should be noted that a dynamic traffic diversion strategy generation matrix is constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic diversion strategy is calculated through a preset cross-platform benefit estimation model. Then, based on the expected user retention growth value of each traffic diversion strategy, a user cross-platform traffic diversion promotion strategy is generated, which specifically includes: Based on the user's cross-platform behavior feature map, a dynamic traffic diversion strategy generation matrix is constructed, and then an initial traffic diversion strategy candidate set is generated based on the dynamic traffic diversion strategy generation matrix; The expected user retention growth value of each candidate strategy in the initial traffic diversion strategy candidate set is calculated through the preset cross-platform benefit estimation model, the calculation results are screened, and then a user cross-platform traffic diversion promotion strategy is generated based on the screening results.

[0024] Specifically, a dynamic drainage strategy generation matrix is constructed based on the user's cross-platform behavior feature map, and then an initial drainage strategy candidate set is generated based on the dynamic drainage strategy generation matrix. The user's cross-platform behavior feature map describes in detail the user's behavior patterns, preferences and identity characteristics on multiple platforms. Based on this, the user's behavior conversion patterns, active time periods, interest preferences and other information between different platforms are analyzed. For example, it is found that the user's attention to a certain type of content on the social platform is related to the purchase behavior on the e-commerce platform, or the user's behavior changes on other platforms after obtaining information on the information platform, etc. Based on the above information, a dynamic drainage strategy generation matrix is constructed. Matrix. The elements of the dynamic traffic diversion strategy generation matrix include strategy-related parameters such as the diversion direction, diversion time, and diversion content between different platforms. For example, the rows of the dynamic traffic diversion strategy generation matrix can represent different platforms, and the columns can represent different diversion strategy dimensions. The values in the dynamic traffic diversion strategy generation matrix can be the weight or probability of diversion, etc. Based on the dynamic traffic diversion strategy generation matrix, an initial traffic diversion strategy candidate set is generated. The initial traffic diversion strategy candidate set includes various possible traffic diversion strategy combinations, such as pushing product information related to e-commerce platforms on social platforms, or guiding users to visit specific content on other platforms on information platforms. The cross-platform benefit estimation model is used to calculate the expected user retention growth value for each candidate strategy in the initial traffic diversion strategy candidate set, and the calculation results are screened. Then, a user cross-platform traffic diversion promotion strategy is generated based on the screening results. The cross-platform benefit estimation model is a data-driven model that comprehensively considers factors such as user behavior data on multiple platforms, platform operation data, and market environment. For each candidate strategy in the initial traffic diversion strategy candidate set, the cross-platform benefit estimation model predicts the user retention growth value that the strategy may bring based on historical data and current user behavior characteristics. For example, for the candidate strategy of "pushing e-commerce platform product information on social platforms", the cross-platform benefit estimation model will analyze the effects of similar strategies in the past, and combine factors such as the current user activity on social platforms, the level of interest in products, and the user retention status of e-commerce platforms to calculate the user retention growth value that the strategy may bring. The calculation results of all candidate strategies are sorted and screened, and a retention growth value threshold is set to retain those strategies with higher expected retention growth values. Based on the screening results, a user cross-platform diversion and promotion strategy is generated. The user cross-platform diversion and promotion strategy can more effectively guide users to interact between multiple platforms and improve user retention and activity. For example, the screened strategies may include pushing content related to popular products on e-commerce platforms on social platforms during a specific time period, or guiding users to visit related social groups or e-commerce pages based on their interests and preferences on information platforms.

[0025] It should be noted that obtaining real-time user feedback data streams, building a dynamic correction model for traffic diversion strategies based on the real-time user feedback data streams, optimizing the user cross-platform traffic diversion and promotion strategies, and deploying the optimized cross-platform traffic diversion and promotion strategies to the target platform to perform user growth operations specifically include: Acquire real-time user feedback data streams through a real-time data collection gateway to build a dynamic monitoring indicator system. The real-time user feedback data streams include behavioral response delay distribution data, conversion funnel parameter data, and sentiment tendency index data. Based on the dynamic monitoring indicator system, abnormal pattern recognition is performed on the real-time user feedback data stream to generate strategy failure warning signals and preference deviation detection reports; Based on the strategy failure warning signal and preference drift detection report, a dynamic correction model for the traffic diversion strategy is constructed, and based on the dynamic correction model for the traffic diversion strategy, the user cross-platform traffic diversion and promotion strategy is optimized to generate an optimized traffic diversion and promotion strategy, and the optimized traffic diversion and promotion strategy is integrated with the user cross-platform traffic diversion and promotion strategy to generate a final cross-platform traffic diversion and promotion strategy, and the final cross-platform traffic diversion and promotion strategy is synchronized to the target platform through a distributed deployment mechanism to execute user growth operations.

[0026] Specifically, real-time user feedback data streams are obtained through a real-time data collection gateway to build a dynamic monitoring indicator system. The real-time user feedback data stream includes behavioral response delay distribution data, conversion funnel parameter data, and emotional tendency index data. The real-time data collection gateway is deployed at key nodes of each platform to collect various types of user behavior data on the platform in real time. Taking the e-commerce platform as an example, the behavioral response delay distribution data records the time distribution from the user clicking on the product to the completion of page loading. The conversion funnel parameter data reflects the conversion rate of each link in the process from browsing the product to placing an order. The emotional tendency index data measures the user's emotional attitude towards the platform and the product by analyzing the user's comments, likes, complaints and other behaviors. After collecting the above data, a dynamic monitoring indicator system is constructed according to the characteristics of different data and analysis needs. The dynamic monitoring indicator system covers indicators of different dimensions, such as the average value of response delay, the change trend of conversion rate, the positive and negative values of emotional tendency, etc., to comprehensively monitor the user's behavior and emotional state on the platform; Secondly, based on the dynamic monitoring indicator system, the real-time user feedback data stream is used to identify abnormal patterns, and strategy failure warning signals and preference shift detection reports are generated. The dynamic monitoring indicator system provides a data basis for abnormal pattern identification. By analyzing the various data in the real-time user feedback data stream and comparing them with the normal range in the indicator system, for example, when the average value of the behavioral response delay exceeds the normal range, or the conversion rate of a certain link in the conversion funnel drops significantly, it is determined to be an abnormal pattern. For the emotional tendency index data, if the user's negative emotional tendency suddenly increases, it is also considered an abnormal situation. Based on the abnormal pattern, a strategy failure warning signal is generated, indicating that there may be problems with the platform's current drainage strategy. At the same time, through in-depth analysis of user behavior data, a preference shift detection report is generated. For example, it is found that the user's preference for a certain type of product has changed, the originally popular products are no longer popular, and new types of products are beginning to be favored. The report describes the changes in user preferences in detail and provides a basis for subsequent strategy adjustments; Based on the strategy failure warning signal and preference drift detection report, a dynamic correction model for traffic diversion strategy is constructed, and based on the dynamic correction model for traffic diversion strategy, the user cross-platform traffic diversion promotion strategy is optimized to generate an optimized traffic diversion promotion strategy, and the optimized traffic diversion promotion strategy is integrated with the user cross-platform traffic diversion promotion strategy to generate the final cross-platform traffic diversion promotion strategy, and the final cross-platform traffic diversion promotion strategy is synchronized to the target platform through a distributed deployment mechanism to execute user growth operations. The dynamic correction model for traffic diversion strategy combines the strategy failure warning signal and the preference drift detection report to analyze the shortcomings of the current traffic diversion strategy. For example, according to the problem of long behavioral response delay in the strategy failure warning signal, the dynamic correction model for traffic diversion strategy may adjust the traffic diversion strategy, optimize the page loading speed or adjust the timing of pushing content, for preference drift Based on the changes in user preferences for products in the detection report, the dynamic correction model of the traffic diversion strategy will adjust the traffic diversion content according to the new preference trend, and push product information that meets the user's new preferences. Based on the dynamic correction model of the traffic diversion strategy, the user's cross-platform traffic diversion and promotion strategy will be optimized to generate an optimized traffic diversion and promotion strategy. The optimized traffic diversion and promotion strategy will be integrated with the original user cross-platform traffic diversion and promotion strategy. Taking into account the advantages of the two strategies, the final cross-platform traffic diversion and promotion strategy will be generated. Through the distributed deployment mechanism, the final cross-platform traffic diversion and promotion strategy will be synchronized to each target platform to ensure that the strategy can be accurately executed on different platforms to achieve the goal of user growth. For example, the optimized traffic diversion and promotion strategy will be executed simultaneously on social platforms and e-commerce platforms to guide users to interact between the two platforms and improve user activity and retention rate.

[0027] Furthermore, the construction process of the dynamic correction model of the diversion strategy specifically includes: Generate a behavioral response correction factor based on the behavioral response delay distribution data in the real-time user feedback data stream, and build a user preference shift detection model based on the conversion funnel parameter data and sentiment index data. Based on the user preference shift detection model, classify and identify abnormal patterns in the real-time user feedback data stream, generate a strategy failure warning signal and a preference shift detection report, and confirm the preference shift correction matrix based on the preference shift detection report. A policy gradient update module is designed to define the policy optimization objective based on the reward function, and then generate the gradient update rule. Then, a dynamic correction model of the diversion strategy is generated by integrating the behavior response correction factor, the preference offset correction matrix and the gradient update rule.

[0028] Specifically, a behavior response correction factor is generated based on the behavior response delay distribution data in the real-time user feedback data stream. The real-time user feedback data stream contains various behavior data of users on the platform. The behavior response delay distribution data reflects the time relationship between user operations and system responses. For example, on an e-commerce platform, the time from when a user clicks on a product details page to when the page is fully loaded, or the time from when an order is submitted to when the system confirms the order, etc. By analyzing the behavior response delay distribution data, statistics such as the average delay and the delay standard deviation are calculated. Based on the statistical information, a behavior response correction factor is generated. For example, if the average delay is too long, a correction factor greater than 1 may be generated, which can be used to subsequently adjust the traffic diversion strategy to optimize the user experience and reduce the negative impact of delay. A user preference shift detection model is constructed by combining conversion funnel parameter data and sentiment index data. Conversion funnel parameter data shows the conversion rate of each link on the platform, from initial browsing behavior to final conversion behavior (such as purchasing products, registering as a member, etc.). Sentiment index data quantifies users' emotional attitudes towards the platform, products, or services by analyzing users' comments, ratings, likes, and other behaviors. For example, on social platforms, users' likes and comments on specific content can reflect their emotional tendencies. By combining conversion funnel parameter data and sentiment index data and utilizing machine learning algorithms such as neural networks or decision trees, a user preference shift detection model is constructed. The user preference shift detection model can learn from users' behavioral patterns and emotional changes at different stages, thereby identifying shifts in user preferences. For example, if the conversion rate of a certain link in the conversion funnel suddenly drops and the sentiment index shows a negative change, it can be determined that user preferences have shifted, possibly because certain platform features or products do not meet user needs. Based on the user preference shift detection model, abnormal patterns in the real-time user feedback data stream are classified and identified, and a strategy failure warning signal and a preference shift detection report are generated. Based on the preference shift detection report, a preference shift correction matrix is confirmed. The user preference shift detection model monitors the real-time user feedback data stream in real time and identifies abnormal patterns. For example, when a user's browsing and purchasing behavior on the e-commerce platform is abnormal, such as frequent browsing but few purchases, or purchasing behavior is concentrated on a few products, the user preference shift detection model classifies these abnormal patterns and generates a strategy failure warning signal based on the classification results, indicating that there may be problems with the platform's current traffic diversion strategy. At the same time, a preference shift detection report is generated, which details the user preference shift, including information such as the direction and degree of the shift. Based on the preference shift detection report, the reasons for the user preference shift are analyzed and a preference shift correction matrix is confirmed. For example, if it is found that the user preference has shifted from one category of goods to another, the preference shift correction matrix can include adjustment strategies for different product categories, such as adjusting the product recommendation algorithm and optimizing the page layout. A policy gradient update module is designed to define the policy optimization goal based on the reward function, and then generate gradient update rules. Then, a dynamic correction model of the drainage strategy is generated by integrating the behavior response correction factor, preference offset correction matrix and gradient update rules. The policy gradient update module is a component used to optimize the drainage strategy. By defining the reward function, the effectiveness of the drainage strategy is measured. The reward function can consider indicators such as user conversion rate, retention rate, and activity. Based on the reward function, the policy optimization goal is determined, such as improving conversion rate or increasing user activity. According to the policy optimization goal, the gradient update rule is generated, and the parameters of the drainage strategy are adjusted using algorithms such as gradient descent. The behavior response correction factor, preference offset correction matrix and gradient update rule are integrated into the dynamic correction model of the drainage strategy. For example, when the behavior response delay is too long, the behavior response correction factor will prompt the model to adjust the policy parameters. Combined with the adjustment strategy for user preference offset in the preference offset correction matrix, the strategy is optimized according to the gradient update rule, thereby generating a more effective drainage strategy and improving the platform's operational effectiveness. Example 2

[0029] Reference Figure 2 , a cross-platform user traffic and growth promotion method for large companies based on artificial intelligence, including the following steps: Obtaining user cross-platform interaction behavior data, inputting the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling, and generating a multi-platform user behavior feature vector set; Performing cross-platform user identity code matching processing on the multi-platform user behavior feature vector set, and establishing a unified cross-platform identity profile of the user based on the processing result, and then conducting platform behavior preference mining on the target user group based on the cross-platform unified identity profile to generate a cross-platform user behavior feature map; A dynamic traffic diversion strategy generation matrix is constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic diversion strategy is calculated through a preset cross-platform benefit estimation model, and then a user cross-platform traffic diversion promotion strategy is generated based on the expected user retention growth value of each traffic diversion strategy; Obtain real-time user feedback data stream, and build a dynamic correction model for traffic diversion strategy based on the real-time user feedback data stream, optimize the user cross-platform traffic diversion and promotion strategy, and deploy the optimized cross-platform traffic diversion and promotion strategy to the target platform to perform user growth operations.

[0030] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the concept of the invention, they should all fall within the scope of protection of the present invention.

[0031] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0032] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention.

Claims

1. An AI-based cross-platform user traffic generation and growth promotion system for large companies, featuring: include: A data collection and processing module is used to obtain user cross-platform interaction behavior data, input the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling, and generate a multi-platform user behavior feature vector set; A data mining module is used to perform cross-platform user identity code matching processing on the multi-platform user behavior feature vector set, and establish a unified cross-platform identity profile of the user based on the processing result, and then conduct platform behavior preference mining on the target user group based on the cross-platform unified identity profile to generate a cross-platform user behavior feature map; A promotion strategy generation module is used to construct a dynamic traffic diversion strategy generation matrix based on the user cross-platform behavior feature map, calculate the expected user retention growth value of each traffic diversion strategy through a preset cross-platform benefit estimation model, and then generate a user cross-platform traffic diversion promotion strategy based on the expected user retention growth value of each traffic diversion strategy; The optimization module is used to obtain real-time user feedback data streams, and build a dynamic correction model for traffic diversion strategies based on the real-time user feedback data streams, optimize the user cross-platform traffic diversion and promotion strategies, and deploy the optimized cross-platform traffic diversion and promotion strategies to the target platform to perform user growth operations.

2. The AI-based large-scale cross-platform user traffic diversion and growth promotion system according to claim 1 is characterized in that: Inputting the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling and generate a multi-platform user behavior feature vector set, specifically including: Establish a cross-platform data collection channel, and based on the cross-platform data collection channel, collect user cross-platform interaction behavior data corresponding to multiple platforms through encrypted tracking technology. The user cross-platform interaction behavior data includes real-time click trajectory data, page dwell time data, and cross-application jump path data; Aggregate the collected user cross-platform interaction behavior data, and then generate a user interaction behavior feature set based on the aggregation processing results, wherein the user interaction behavior feature set includes user device fingerprint features, cross-platform account association features, and behavior time series parameter features; Constructing a behavior feature deep extraction network, performing multi-dimensional feature calculation on the user interaction behavior feature set based on the behavior feature deep extraction network, and then generating a primary feature set; A cross-platform feature association model is performed on the primary feature set, and the weight distribution corresponding to each platform is calculated, and then the weight distribution corresponding to each platform is integrated to obtain a multi-platform user behavior feature vector set.

3. The AI-based large-scale cross-platform user traffic diversion and growth promotion system according to claim 2 is characterized in that: Perform cross-platform user identity code matching on the multi-platform user behavior feature vector set, and establish a unified cross-platform user identity profile based on the processing result. Then, conduct platform behavior preference mining on the target user group based on the cross-platform unified identity profile to generate a cross-platform user behavior feature map, specifically including: Build a user identity matching decision set based on the user's device fingerprint features and cross-platform account association features, perform feature migration on the user's cross-platform interaction behavior data corresponding to multiple platforms, generate a user authentication feature set, and then match the user identity matching decision set with the user authentication feature set to establish a unified cross-platform identity profile for the user; Construct a dynamic preference prediction model, extract spatiotemporal features of the user's unified identity portrait across platforms, and generate a behavioral preference prediction map; The user's unified cross-platform identity portrait and behavioral preference prediction map are projected into three-dimensional space and time to generate a user cross-platform behavioral feature map.

4. The AI-based large-scale cross-platform user traffic diversion and growth promotion system according to claim 1 is characterized in that: A dynamic traffic diversion strategy generation matrix is constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic diversion strategy is calculated using a preset cross-platform benefit estimation model. Then, based on the expected user retention growth value of each traffic diversion strategy, a user cross-platform traffic diversion promotion strategy is generated, specifically including: Based on the user's cross-platform behavior feature map, a dynamic traffic diversion strategy generation matrix is constructed, and then an initial traffic diversion strategy candidate set is generated based on the dynamic traffic diversion strategy generation matrix; The expected user retention growth value of each candidate strategy in the initial traffic diversion strategy candidate set is calculated through the preset cross-platform benefit estimation model, the calculation results are screened, and then a user cross-platform traffic diversion promotion strategy is generated based on the screening results.

5. The AI-based large-scale cross-platform user traffic diversion and growth promotion system according to claim 1 is characterized in that: Obtain real-time user feedback data streams, and build a dynamic correction model for traffic diversion strategies based on the real-time user feedback data streams, optimize the user cross-platform traffic diversion and promotion strategies, and deploy the optimized cross-platform traffic diversion and promotion strategies to the target platform to perform user growth operations, specifically including: Acquire real-time user feedback data streams through a real-time data collection gateway to build a dynamic monitoring indicator system. The real-time user feedback data streams include behavioral response delay distribution data, conversion funnel parameter data, and sentiment tendency index data. Based on the dynamic monitoring indicator system, abnormal pattern recognition is performed on the real-time user feedback data stream to generate strategy failure warning signals and preference deviation detection reports; Based on the strategy failure warning signal and preference drift detection report, a dynamic correction model for the traffic diversion strategy is constructed, and based on the dynamic correction model for the traffic diversion strategy, the user cross-platform traffic diversion and promotion strategy is optimized to generate an optimized traffic diversion and promotion strategy, and the optimized traffic diversion and promotion strategy is integrated with the user cross-platform traffic diversion and promotion strategy to generate a final cross-platform traffic diversion and promotion strategy, and the final cross-platform traffic diversion and promotion strategy is synchronized to the target platform through a distributed deployment mechanism to execute user growth operations.

6. The AI-based large-scale cross-platform user traffic diversion and growth promotion system according to claim 5 is characterized in that: The construction process of the dynamic correction model of the diversion strategy includes: Generate a behavioral response correction factor based on the behavioral response delay distribution data in the real-time user feedback data stream, and build a user preference shift detection model based on the conversion funnel parameter data and sentiment index data. Based on the user preference shift detection model, classify and identify abnormal patterns in the real-time user feedback data stream, generate a strategy failure warning signal and a preference shift detection report, and confirm the preference shift correction matrix based on the preference shift detection report. A policy gradient update module is designed to define the policy optimization objective based on the reward function, and then generate the gradient update rule. Then, a dynamic correction model of the drainage strategy is generated by integrating the behavior response correction factor, the preference offset correction matrix and the gradient update rule.

7. The AI-based cross-platform user traffic diversion and growth promotion method for large companies is applied to the AI-based cross-platform user traffic diversion and growth promotion system for large companies as described in any one of claims 1-6, characterized in that: The following steps are involved: Obtaining user cross-platform interaction behavior data, inputting the user cross-platform interaction behavior data into a behavior feature deep extraction network to perform cross-platform feature association modeling, and generating a multi-platform user behavior feature vector set; Performing cross-platform user identity code matching processing on the multi-platform user behavior feature vector set, and establishing a unified cross-platform identity profile of the user based on the processing result, and then conducting platform behavior preference mining on the target user group based on the cross-platform unified identity profile to generate a cross-platform user behavior feature map; A dynamic traffic diversion strategy generation matrix is constructed based on the user cross-platform behavior feature map, and the expected user retention growth value of each traffic diversion strategy is calculated through a preset cross-platform benefit estimation model, and then a user cross-platform traffic diversion promotion strategy is generated based on the expected user retention growth value of each traffic diversion strategy; Obtain real-time user feedback data stream, and build a dynamic correction model for traffic diversion strategy based on the real-time user feedback data stream, optimize the user cross-platform traffic diversion and promotion strategy, and deploy the optimized cross-platform traffic diversion and promotion strategy to the target platform to perform user growth operations.

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