Digital integral synchronization method based on multi-source platform

Abstract

The invention discloses a digital point synchronization method based on a multi-source platform, and relates to the technical field of digital point management, and the method comprises the specific steps: S100, data collection and integration: for a plurality of target platforms, such as an e-commerce platform, a social platform and a content platform, applying a web crawler technology and application programming interfaces provided by the platforms, according to the method, the behavior data of the user on multiple target platforms such as the e-commerce platform, the social platform and the content platform are collected, the data processing and user portrait construction technology is utilized, information of behavior habits, interest preferences and value levels of the user is deeply mined, and the comprehensive user behavior analysis has the advantages that the user experience is improved, and the user experience is improved. According to the method and the system, powerful support is provided for integration synchronization, the cross-platform activity and loyalty of the user are improved, meanwhile, the participation enthusiasm of the user is further stimulated through integration pre-distribution and integration linkage reward mechanisms, and interaction and cooperation between platforms are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of digital point management, and in particular to a digital point synchronization method based on a multi-source platform. Background Art

[0002] With the vigorous development of the Internet, e-commerce platforms, social platforms, content platforms and multi-source platforms have become an important part of people's daily lives. These platforms provide users with personalized services and experiences by accumulating a large amount of user behavior data. In the digital age, points, as an important user incentive mechanism, are widely used in various platforms to enhance user activity, promote user consumption and enhance user stickiness. However, the existing point synchronization methods are mostly limited to a single platform and cannot achieve cross-platform point accumulation and redemption, which limits the effectiveness of the point incentive mechanism.

[0003] Traditional points synchronization methods have many shortcomings. First, points synchronization methods are often independent of each other, and points between different platforms cannot be used universally, resulting in the inability to form a synergy when users accumulate points on multiple platforms, reducing the efficiency and experience of users' points usage. Secondly, traditional points synchronization methods lack deep insight into user behavior and accurate prediction capabilities. Points allocation is often based on simple consumption behavior or task completion, which cannot accurately reflect users' real needs and value contributions. In addition, traditional points synchronization methods also have many challenges in terms of points synchronization and updating, such as inconsistent points data and expired points, which further affect the effectiveness of the points incentive mechanism and user satisfaction.

[0004] Therefore, the development of a digital points synchronization method based on a multi-source platform provides strong support for building a more efficient, convenient and personalized user incentive mechanism. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide a digital points synchronization method based on a multi-source platform. The method realizes the synchronization and precise management of user points among multiple platforms through six core steps: data collection and integration, user portrait construction, behavior prediction and points pre-allocation, behavior association and points linkage, points synchronization and update, and user feedback and optimization. It uses web crawler technology, user similarity score calculation, and behavior value evaluation formula technical means to deeply mine user behavior data, build a comprehensive user portrait, and predict the user's future behavior based on this, and pre-allocate points for the user. At the same time, through the behavior association model and trigger rules, the linkage reward of points among different platforms is realized, thereby improving user activity and platform revenue.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a digital integration synchronization method based on a multi-source platform, the specific steps of the synchronization method are:

[0007] S100, data collection and integration: for multiple target platforms such as e-commerce platforms, social platforms, and content platforms, use web crawler technology and the application programming interface provided by the platform to collect user behavior data, clean the collected data, remove invalid and duplicate data, unify the data format through standardized conversion rules, and store it in the central data repository;

[0008] S200, user portrait construction: extract the sorted and pre-processed data from the central database storing user behavior data, divide the users into groups by calculating the user similarity scores, and after completing the user group division, introduce the user behavior value evaluation formula to quantitatively evaluate the user's behavior on each platform, and construct a user portrait with multi-dimensional information through the user portrait fusion formula, and update it regularly with new data;

[0009] S300, behavior prediction and points pre-allocation: Based on the user portrait, the behavior prediction formula is used to predict the user's future behavior, and points are pre-allocated to the user using the points pre-allocation formula on the corresponding platform according to the prediction results;

[0010] S400, behavior association and points linkage: mining multi-source platform user behavior data, and establishing a correlation model between user behaviors on different platforms through the behavior association strength formula, the formula is: Among them, g k (B i，p1 , B j，p2 ) is the kth association metric function, N is the total number of association metric functions, μ k is the weight of the kth association metric function. The behavior association rules are established. When a user completes a specific behavior on one platform, the points linkage reward on other platforms is automatically triggered according to the triggering rules. The points linkage reward points on other platforms are calculated by combining the points pre-allocation optimization formula. The formula is: LR i，p2 =τ×S(B i，p1 , B j，p2 )×PI i，p1 ×φ p2 , where LR i，p2 is the number of points linkage rewards, τ is the reward adjustment factor, φ p2 is the integral linkage coefficient of platform p2;

[0011] S500, points synchronization and update, establish a message queue mechanism between multiple source platforms, use Apache Kafka to carry and transmit points change information, monitor the points acquisition and consumption of users on each platform in real time, and when the points of a user on a certain platform change, synchronize the points change information to other related platforms in a timely manner according to the synchronization rules. Other related platforms process according to the preset rules and regularly review the number of points. If there are abnormalities in the points data or the pre-allocated points do not match the actual points, automatically adjust the points value or trigger the supplementary points mechanism to correct them;

[0012] S600, user feedback and optimization: Set up various feedback channels on each platform to collect users' evaluation and suggestions on various aspects of the points synchronization method, analyze the feedback content and suggestions, and optimize each link of the points synchronization method.

[0013] Furthermore, in the S200, in the user portrait construction, the users are divided into groups by calculating the user similarity scores, and the formula is: Among them: UCS ij represents the user clustering similarity score between user i and user j, K is the number of feature types selected to measure user similarity, and λ k is the weight coefficient of the Kth feature, T is the time window length used to calculate the feature, x ikt is the value of user i on the kth feature at time t is the average value of all users on the kth feature.

[0014] Furthermore, in the S200, the user profile construction measures the value of the user on each platform through the user behavior value evaluation, and the calculation formula is: Among them, F ij，p represents the frequency of user i performing the jth behavior on platform p, W j，p is the weight that platform p assigns to the behavior, I ik，p is the intensity of the kth interactive behavior generated by user i on platform p, V k，p is the value coefficient of this interaction behavior on platform p, A i，p is the active time of user i on platform p, and α, β, and γ are balance coefficients.

[0015] Furthermore, in the S200, a comprehensive user portrait is constructed by using a user portrait fusion formula in the user portrait construction, and the formula is: Among them BV i，p is the behavior value of user i on platform p, PP p is the eigenvector of platform p, E p is the correlation vector between platform p and other platforms, λ pis the fusion coefficient of platform p, and p is the total number of platforms included in the integral synchronization system.

[0016] Furthermore, in the above S300, the user comprehensive profile UP is used in the behavior prediction and point pre-allocation. i and current environmental factors C t , use the behavior prediction formula to predict the user's future behavior, the formula is: Among them, f l (UP i , C t ) is the lth feature function, and the user profile UP i is a vector (x1, x2, ..., x m ), environmental factors C t is a vector (y1, y2, ..., y n ), the formula is: n and m are the number of user portrait vectors and environmental factor vectors respectively, ω l is the weight of the lth feature function, and σ(x) is the Sigmoid function.

[0017] Furthermore, in the above S300, the points pre-allocation formula is used to pre-allocate points for user i on platform p in the behavior prediction and points pre-allocation, and the formula is: Where P(B t+1 |UP i , C t ) is used to calculate the probability of the user's future behavior. R(B i，p ) is the expected profit function that behavior B brings to the platform P, d(B i，p ) is the difficulty coefficient of behavior B, V p is the integral value coefficient of platform P.

[0018] Furthermore, in S300, the expected benefit function R(B i，p ) on different platforms. On the e-commerce platform, if the profit margin of the product is r, the user's purchase frequency of the product is f, and the user's consumption life cycle value is L, then the expected profit function R(B i，p ) is: R(B i，p )=r×f×L; On the social platform, let A be the potential contribution of the user’s social activity to the platform’s advertising revenue, and G be the growth value brought by the user’s invitation of new users. The expected return function R(B i，p ) is: R(B i，p )=A+G; On the content platform, let T be the traffic attracted by users creating high-quality content, and V be the revenue generated by users paying to read or watch high-quality content. Then the expected revenue function R(B i，p ) is: R(Bi，p )=T+V.

[0019] Furthermore, in S400, the correlation metric function g in the behavior correlation and integral linkage k (B i，p1 , B j，p2 ) The specific mathematical expression is, when it is the inverse function of the time interval between behaviors, the expression is: Among them, t ij Complete behavior B for the user on platform p1 i and complete behavior B on platform p2 j time interval; when is the behavior content semantic similarity function based on natural language processing technology, the behavior-related text is converted into vector representation through the pre-trained word vector model, let V i and V j Behavior B i，p1 and B j，p2 The vector after the relevant text is converted is expressed as: When is the user behavior transition probability function, the expression is: Among them, n ij For behavior B on platform p1 i Behavior B transferred to platform p2 j The frequency, ∑ k n ik For behavior B on platform p1 i Total frequency of transfer behavior to all platforms.

[0020] Furthermore, the triggering rules in the behavior association and points linkage in S400 are:

[0021] Rules for associating e-commerce platform purchases with content platform learning: If a user completes a purchase of goods related to a specific interest area on an e-commerce platform, the content platform will push high-quality articles, video tutorials, and learning materials closely related to that interest area to the user, and set points rewards;

[0022] Rules for associating content platform reading with e-commerce platform consumption: When users deeply read or browse articles or video content related to a specific consumption field on a content platform, and their browsing behavior meets the depth index, the e-commerce platform will accurately recommend products related to the content to them. Users can get points rewards by clicking to view the recommended product details page within a certain period of time.

[0023] Rules for associating purchases on e-commerce platforms with sharing on social platforms: When a user completes a product purchase on an e-commerce platform, a personalized dynamic message about the user's purchase of the product is automatically generated on the social platform. After the user completes the sharing operation, he or she can receive points as a reward.

[0024] Rules for associating attention on content platforms with interactions on social platforms: Users follow specific hot topics, experts in the field, and interest groups on content platforms, and are recommended to join discussion groups and online activities related to the topic on social platforms. Users who participate in group discussions and interact with activities to a certain extent can receive points rewards;

[0025] Rules for associating social platform interactions with e-commerce platform discounts: When users participate in specific interactive activities held by the social platform and the interaction effect reaches a certain level, the e-commerce platform will provide users with exclusive discounts on products related to the brand or activity. Users can receive points as rewards after using these discounts for shopping.

[0026] Furthermore, in the S500, the integral synchronization and update uses the integral synchronization consistency formula to review the integral, and the formula is: Among them, CS is the consistency index, is the theoretical score that user i should have on platform p, I i，p is the actual score, p is the total number of platforms, M is the total number of users, and when CS exceeds the threshold T CS , start automatically adjusting the integral value or trigger the supplementary integral mechanism for correction.

[0027] Compared with the prior art, this digital integration synchronization method based on a multi-source platform has the following beneficial effects:

[0028] 1. The present invention collects user behavior data on multiple target platforms such as e-commerce platforms, social platforms, and content platforms, and uses data processing and user portrait construction technology to deeply explore users' behavioral habits, interest preferences, and value levels. This comprehensive user behavior analysis provides strong support for the synchronization of points, improves users' cross-platform activity and loyalty, and at the same time, through the point pre-allocation and point linkage reward mechanism, it further stimulates users' enthusiasm for participation and promotes interaction and cooperation between platforms.

[0029] 2. The present invention mines user behavior data from multiple platforms, establishes a correlation model between user behaviors on different platforms, and reveals the inherent logical relationship between behaviors on different platforms. By setting flexible trigger rules, when a user completes a specific behavior on one platform, it can automatically trigger the point linkage reward on other platforms. This not only promotes the flow and interaction of users between different platforms, but also realizes the cross-platform sharing and appreciation of points, thereby enhancing users' sense of gain and satisfaction.

[0030] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 A flowchart of a digital integration synchronization method based on a multi-source platform;

[0033] Figure 2 The figure is a process framework diagram of a digital integration synchronization method based on a multi-source platform. DETAILED DESCRIPTION

[0034] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0035] Embodiment 1

[0036] Points synchronization between e-commerce platforms and content platforms

[0037] Data collection and integration: For e-commerce platforms and content platforms, use web crawler technology to collect user behavior data. For example, collect users' purchase details (including product category, price, and purchase time) on e-commerce platforms, and collect users' reading topics, reading time, and comment content on content platforms. At the same time, obtain more accurate and comprehensive data through the application programming interface provided by the platform, clean the collected data, remove invalid data such as duplicate purchase records or incorrect reading time data, use standardized conversion rules to unify the data format, and store it in the central data repository.

[0038] User portrait construction extracts data from the central database, calculates user similarity scores and divides users into groups. The formula is: For example, for users who are active on both e-commerce and content platforms, similarity scores are calculated based on their behavioral characteristics on the two platforms (such as purchase frequency and reading frequency). Then, the user behavior value evaluation formula is introduced to quantitatively evaluate the user's behavior on each platform. On the e-commerce platform, the behavior value of users who purchase high-value goods with high frequency is higher. The formula is: On content platforms, users’ reading depth and comment participation are of high value. Through the user portrait fusion formula, a structured user portrait containing basic information, behavioral habits, interest preferences, and multi-dimensional information of value levels is constructed. The formula is:

[0039] Behavior prediction and points pre-allocation, based on user portraits, predict user future behavior, and UP through comprehensive user portraits i and current environmental factors C t , use the behavior prediction formula to predict the user's future behavior, the formula is: For example, if the user profile shows that the user is interested in a certain type of product and often reads related articles on the content platform, it is predicted that the user is likely to purchase this type of product. Based on the prediction results, points are pre-allocated to the user using the points pre-allocation formula on the corresponding platform. The formula is: On e-commerce platforms, if it is predicted that a user may purchase a certain product, points will be pre-allocated to the user based on the expected benefits of the product (such as profit margin, user purchase frequency and consumption lifetime value); on content platforms, if it is predicted that a user will continue to read a certain type of article, points will be pre-allocated to the user based on the traffic and revenue brought by the article.

[0040] Behavior association and points linkage are used to explore the association of user behavior data on e-commerce and content platforms. For example, an association model is established through the behavior association strength formula, which is: When it is an association rule between e-commerce platform purchase and content platform learning, if the user purchases a certain type of learning materials (such as English learning materials) on the e-commerce platform and the amount reaches X yuan or more, the content platform recommends relevant learning course content to the user. The user can get points reward by clicking to view the relevant courses. Combined with the calculation of the points pre-allocation optimization formula, the other platform points linkage reward points are calculated. The formula is: LR i，p2 =τ×S(B i，p1 , B j，p2 )×PI i，p1 ×φ p2 .

[0041] Points synchronization and update: connect e-commerce and content platforms to establish a message queue mechanism. When users purchase goods on the e-commerce platform and earn points, the message queue mechanism will synchronize the points change information to the content platform in a timely manner according to the preset synchronization rules. Similarly, when users earn points through reading on the content platform, they will also be synchronized to the e-commerce platform. The points are regularly reviewed through the points synchronization consistency formula. The formula is: If there are anomalies in the points data or the pre-allocated points do not match the actual situation, accurate corrections can be made by automatically adjusting the points value or triggering the supplementary points mechanism.

[0042] To sum up, in the process of synchronizing points between e-commerce platforms and content platforms, by collecting and integrating user behavior data of the two platforms, users are divided into groups based on the user portrait construction formula and their behavior value is quantitatively evaluated, and then a comprehensive user portrait is constructed. The behavior prediction formula is combined with the point pre-allocation formula to pre-allocate points for users on the two platforms. The behavior association strength formula is used to establish a behavior association model between platforms. When specific association rules are met, users are given point rewards based on the point linkage reward quantity formula. Finally, the message queue mechanism is used to synchronize and review the points according to the preset rules and the point synchronization consistency formula to ensure the accuracy and consistency of the point data.

[0043] Embodiment 2:

[0044] Points synchronization between social platforms and e-commerce platforms

[0045] Data collection and integration: For social platforms and e-commerce platforms, we use web crawler technology and the application programming interface provided by the platform to collect user behavior data, collect users' social interaction information (such as likes, comments, shares) and user profile information on social platforms, and collect users' purchasing behavior and product browsing records on e-commerce platforms. We clean the collected data, remove invalid and duplicate data, and store it in a central data repository after unifying the data format.

[0046] User portrait construction, extracting data from the central database, and dividing users into groups by calculating user similarity scores. The formula is: For example, similarity scores are calculated based on the user's social activity on social platforms and purchase activity on e-commerce platforms, and then the user's behavior on each platform is quantitatively evaluated using the user behavior value evaluation formula. On social platforms, users with high social activity and the potential to contribute more advertising revenue or invite new users to bring high growth value have higher behavior value. The formula is: On e-commerce platforms, users with high purchase frequency and high-value goods have higher behavioral value. A structured user portrait containing basic information, behavioral habits, interest preferences, and multi-dimensional information of value levels is constructed through the user portrait fusion formula. The formula is:

[0047] Behavior prediction and points pre-allocation, based on user portraits, predict the user's future behavior. The formula is: For example, if the user portrait shows that the user is highly socially active and often browses a certain type of goods on the e-commerce platform, it is predicted that the user is likely to purchase such goods and share the shopping experience. Based on the prediction results, points are pre-allocated to the user on the corresponding platform using the points pre-allocation formula. The formula is: On e-commerce platforms, points are pre-allocated to users based on the expected returns of goods (such as profit margins, user purchase frequency, and consumption lifetime value); on social platforms, points are pre-allocated to users based on the potential contribution of increased user social activity to the platform's advertising revenue and the growth value brought by inviting new users.

[0048] Behavior association and points linkage are used to explore the association of user behavior data on social platforms and e-commerce platforms, and to establish an association model through the behavior association strength formula. The formula is: For example, when a user purchases a product with social sharing attributes on an e-commerce platform, a personalized dynamic message of the user's purchase of the product is automatically generated on the social platform, encouraging the user to share the shopping experience within a certain time limit. After the user completes the sharing operation, he or she can get points reward. Similarly, when a user participates in a specific interactive activity held by the platform on the social platform, and the interactive effect reaches a certain indicator, the e-commerce platform will provide the user with exclusive discounts for products related to the brand or activity. After the user uses these discounts for shopping, he or she can get points reward. Combined with the calculation of the points pre-allocation optimization formula, the points of other platforms are linked to the reward points. The formula is: LR i，p2 =τ×S(B i，p1 , B j，p2 )×PI i，p1 ×φ p2 .

[0049] Points synchronization and update, connect social platforms and e-commerce platforms to establish a message queue mechanism. When users purchase goods on the e-commerce platform and earn points, the message queue mechanism will synchronize the points change information to the social platform in a timely manner according to the preset synchronization rules. Similarly, when users earn points through interactive behaviors on the social platform, they will also be synchronized to the e-commerce platform. The points are regularly reviewed through the points synchronization consistency formula. The formula is: In the event that there are anomalies in the integral data or the pre-allocated integrals do not match the actual situation, accurate corrections can be made by automatically adjusting the integral value or acting as a supplementary integral mechanism.

[0050] To summarize, for the synchronization of points between social platforms and e-commerce platforms, we first collect and integrate user behavior data from the two platforms, use user portrait construction related formulas to complete the construction of user portraits, pre-allocate points on the corresponding platforms based on user portraits through behavior prediction formulas and point pre-allocation formulas, and mine the behavioral associations between platforms through the behavior association strength formula. Based on the trigger rules and the point linkage reward quantity formula, point linkage rewards are implemented. Finally, the message queue mechanism and the point synchronization consistency formula are used to ensure the synchronization of points between the two platforms and the correct review of data.

[0051] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A digital integration synchronization method based on a multi-source platform, characterized in that: The specific steps of this synchronization method are: S100, data collection and integration: for multiple target platforms such as e-commerce platforms, social platforms, and content platforms, use web crawler technology and the application programming interface provided by the platform to collect user behavior data, clean the collected data, remove invalid and duplicate data, unify the data format through standardized conversion rules, and store it in the central data repository; S200, user portrait construction: extract the sorted and pre-processed data from the central database storing user behavior data, divide the users into groups by calculating the user similarity scores, and after completing the user group division, introduce the user behavior value evaluation formula to quantitatively evaluate the user's behavior on each platform, and construct a user portrait with multi-dimensional information through the user portrait fusion formula, and update it regularly with new data; S300, behavior prediction and points pre-allocation: Based on the user portrait, the behavior prediction formula is used to predict the user's future behavior, and points are pre-allocated to the user using the points pre-allocation formula on the corresponding platform according to the prediction results; S400, behavior association and points linkage: mining multi-source platform user behavior data, and establishing a correlation model between user behaviors on different platforms through the behavior association strength formula, the formula is: Among them, g k (B i，p1 , B j，p2 ) is the kth association metric function, N is the total number of association metric functions, μ k is the weight of the kth association metric function. The behavior association rules are established. When a user completes a specific behavior on one platform, the points linkage reward on other platforms is automatically triggered according to the triggering rules. The points linkage reward points on other platforms are calculated by combining the points pre-allocation optimization formula. The formula is: LR i，p2 =τ×S(B i，p1 , B j，p2 )×PI i，p1 ×φ p2 , where LR i，p2 is the number of points linkage rewards, τ is the reward adjustment factor, φ p2 is the integral linkage coefficient of platform p2; S500, points synchronization and update, establish a message queue mechanism between multiple source platforms, use Apache Kafka to carry and transmit points change information, monitor the points acquisition and consumption of users on each platform in real time, and when the points of a user on a certain platform change, synchronize the points change information to other related platforms in a timely manner according to the synchronization rules. Other related platforms process according to the preset rules and regularly review the number of points. If there are abnormal points data or the pre-allocated points do not match the actual points, automatically adjust the points value or trigger the supplementary points mechanism to correct it; S600, user feedback and optimization: Set up various feedback channels on each platform to collect users' evaluation and suggestions on various aspects of the points synchronization method, analyze the feedback content and suggestions, and optimize each link of the points synchronization method.

2. A digital integration synchronization method based on a multi-source platform according to claim 1, characterized in that: In the above S200, in the construction of user portraits, users are divided into groups by calculating user similarity scores, and the formula is: Among them: UCS ij represents the user clustering similarity score between user i and user j, K is the number of feature types selected to measure user similarity, and λ k is the weight coefficient of the Kth feature, T is the time window length used to calculate the feature, x ikt is the value of user i on the kth feature at time t is the average value of all users on the kth feature.

3. A digital integration synchronization method based on a multi-source platform according to claim 1, characterized in that: In S200, the user's value on each platform is measured by evaluating the user's behavior value in the construction of the user portrait. The calculation formula is: Among them, F ij，p represents the frequency of user i performing the jth behavior on platform p, W j，p is the weight that platform p assigns to the behavior, I ik，p is the intensity of the kth interactive behavior generated by user i on platform p, V k，p is the value coefficient of this interaction behavior on platform p, A i，p is the active time of user i on platform p, and α, β, and γ are balance coefficients.

4. The digital integration synchronization method based on a multi-source platform according to claim 1, characterized in that: In the above S200, in the user portrait construction, a comprehensive user portrait is constructed by using a user portrait fusion formula, and the formula is: Among them BV i，p is the behavior value of user i on platform p, PP p is the eigenvector of platform p, E p is the correlation vector between platform p and other platforms, λ p is the fusion coefficient of platform p, and p is the total number of platforms included in the integral synchronization system.

5. The digital integration synchronization method based on a multi-source platform according to claim 1, characterized in that: In S300, behavior prediction and point pre-allocation are performed by using a comprehensive user profile UP i and current environmental factors C t , use the behavior prediction formula to predict the user's future behavior, the formula is: Among them, f l (UP i , C t ) is the lth feature function, and the user profile UP i is a vector (x1, x2, ..., x m ), environmental factors C t is a vector (y1, y2, ..., y n ), the formula is: n and m are the number of user portrait vectors and environmental factor vectors respectively, ω1 is the weight of the lth feature function, and σ(x) is the Sigmoid function.

6. A digital integration synchronization method based on a multi-source platform according to claim 1, characterized in that: In the above S300, the points pre-allocation formula is used to pre-allocate points for user i on platform p in behavior prediction and points pre-allocation. The formula is: Where P(B t+1 |UP i , C t ) is used to calculate the probability of the user's future behavior. R(B i，p ) is the expected profit function that behavior B brings to the platform P, d(B i，p ) is the difficulty coefficient of behavior B, V p is the integral value coefficient of platform P.

7. A digital integration synchronization method based on a multi-source platform according to claim 6, characterized in that: S300, the expected benefit function R(B) in behavior prediction and point pre-allocation i，p ) on different platforms. On the e-commerce platform, if the profit margin of the product is r, the user's purchase frequency of the product is f, and the user's consumption life cycle value is L, then the expected profit function R(B i，p ) is: R(B i，p )=r×f×L; On the social platform, let A be the potential contribution of the user’s social activity to the platform’s advertising revenue, and G be the growth value brought by the user’s invitation of new users. The expected return function R(B i，p ) is: R(B i，p ) = A + G; On the content platform, let T be the traffic attracted by users creating high-quality content, and V be the revenue generated by users paying to read or watch high-quality content. Then the expected revenue function R(B i，p ) is: R(B i，p )=T+V.

8. The digital integration synchronization method based on a multi-source platform according to claim 1, characterized in that: In S400, the correlation metric function g in the behavior correlation and integral linkage k (B i，p1 , B j，p2 ) The specific mathematical expression is, when it is the inverse function of the time interval between behaviors, the expression is: Among them, t ij Complete behavior B for the user on platform p1 i and complete behavior B on platform p2 j time interval; when is the behavior content semantic similarity function based on natural language processing technology, the behavior-related text is converted into vector representation through the pre-trained word vector model, let V i and V j Behavior B i，p1 and B j，p2 The vector after the relevant text is converted is expressed as: When is the user behavior transition probability function, the expression is: Among them, n ij For behavior B on platform p1 i Behavior B transferred to platform p2 j The frequency, Σ k n ik For behavior B on platform p1 i Total frequency of transfer behavior to all platforms.

9. The digital integration synchronization method based on a multi-source platform according to claim 1, characterized in that: In S400, the triggering rules in the behavior association and points linkage are: Rules for associating e-commerce platform purchases with content platform learning: If a user completes a purchase of goods related to a specific interest area on an e-commerce platform, the content platform will push high-quality articles, video tutorials, and learning materials closely related to that interest area to the user, and set points rewards; Rules for associating content platform reading with e-commerce platform consumption: When users deeply read or browse articles or video content related to a specific consumption field on a content platform, and their browsing behavior meets the depth index, the e-commerce platform will accurately recommend products related to the content to them. Users can get points rewards by clicking to view the recommended product details page within a certain period of time. Rules for associating purchases on e-commerce platforms with sharing on social platforms: When a user completes a product purchase on an e-commerce platform, a personalized dynamic message about the user's purchase of the product is automatically generated on the social platform. After the user completes the sharing operation, he or she can receive points as a reward. Rules for associating attention on content platforms with interactions on social platforms: Users follow specific hot topics, experts in the field, and interest groups on content platforms, and are recommended to join discussion groups and online activities related to the topic on social platforms. Users who participate in group discussions and interact with activities to a certain extent can receive points rewards; Rules for associating social platform interactions with e-commerce platform discounts: When users participate in specific interactive activities held by the social platform and the interaction effect reaches a certain level, the e-commerce platform will provide users with exclusive discounts on products related to the brand or activity. Users can receive points as rewards after using these discounts for shopping.

10. The digital integration synchronization method based on a multi-source platform according to claim 1, characterized in that: In S500, the integral synchronization and update uses the integral synchronization consistency formula to review the integral, and the formula is: Among them, CS is the consistency index, is the theoretical score that user i should have on platform p, I i，p is the actual score, p is the total number of platforms, M is the total number of users, and when CS exceeds the threshold T CS , start automatically adjusting the integral value or trigger the supplementary integral mechanism for correction.

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