User contribution-based gift buying and commission rebating method and layered cash rebating system

Abstract

The invention relates to the technical field of shopping services, in particular to a user contribution-based donation rebate method and a layered cash rebate system. According to the technical scheme, encryption collection points are arranged at multiple positions of an e-commerce platform by using a block chain technology, and multi-source data such as user purchase, social recommendation and content creation are collected and safely stored. And performing deep analysis on the data by using an artificial intelligence algorithm, and performing intelligent layering according to a user contribution evaluation model. And formulating differentiated buying and commission rebating strategies according to user levels, and dynamically adjusting in combination with factors such as markets and the like. And when the user recommends that the new user succeeds in purchasing, the system carries out real-time commission rebate and issues gifts based on the block chain smart contract. And data are collected and analyzed again according to a set period, the user hierarchy is dynamically updated, and a visual report is provided. Through multi-source data acquisition, intelligent layering, dynamic strategy making and the like, the data security is guaranteed, the user experience is improved, and the platform competitiveness and the intelligent level are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of shopping services, and in particular to a buy-and-give-away commission method and a tiered cashback system based on user contributions. Background Art

[0002] In today's booming e-commerce era, shopping services have become an indispensable part of people's lives. To attract users, increase sales, and maintain user retention, e-commerce platforms widely adopt various marketing methods, among which buy-one-get-one-free commission rebates are a common and effective method.

[0003] Traditional e-commerce buy-get-one-rebate models primarily determine gifts and commissions based on a simple purchase amount or quantity. For example, users may receive a gift after spending a certain amount of money, or receive a fixed commission percentage for recommending new users. However, this approach has many limitations.

[0004] After searching, the patent with patent announcement number 202311688152.0 discloses a rebate method, device, computer-readable storage medium and electronic device. Although the patent discloses a rebate method, which is applied to the target platform, including obtaining transaction information of multiple merchants according to the first time period, and determining the agent corresponding to each merchant, and determining the rebate rules based on the contract information between the agent and the target platform, and then calculating the agent's rebate amount and making the rebate, the device has obvious deficiencies when used. First, the data collection dimension is extremely limited, focusing only on the merchant's transaction information, and does not involve the rich and diverse behavioral data of users on the platform, such as social recommendation behavior, content creation and interaction data in the platform community, etc., and it is impossible to fully and deeply understand the true value of users and their contribution to the platform ecology, and it is difficult to accurately evaluate the potential influence of different user groups. Second, there is a lack of an effective mechanism for intelligent user segmentation. This completely fails to account for the dynamic changes in the market environment and the evolving user behavior patterns. The platform cannot flexibly segment users based on these factors, resulting in an inability to provide personalized services tailored to the needs of different user tiers and a struggle to meet users' increasingly diverse consumption expectations. Third, the rebate strategy lacks the necessary flexibility and dynamic adjustment capabilities. It fails to formulate differentiated rebate plans based on real-time market demand, actual product inventory levels, and product lifecycle stages. This makes it difficult to achieve a balance between product sales and inventory management. This can lead to inventory overstocking or stockouts in actual operations, impacting the platform's operational efficiency and economic benefits. Fourth, there are significant shortcomings in data security and transaction fairness. Advanced technologies such as blockchain are not utilized to ensure data security and transaction fairness. User data may be exposed to the risk of leakage during transmission and storage. Furthermore, the rebate process may be subject to human interference, resulting in inaccurate calculation of rebate amounts or untimely payment, harming the interests of users and agents and reducing the platform's credibility. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a purchase-and-give-back commission method and a tiered cashback system based on user contribution, which solves the problems raised in the background technology.

[0006] The present invention solves the above-mentioned technical problems in the following ways:

[0007] A method for providing a buy-and-give commission based on user contributions, comprising the following steps:

[0008] S1. Multi-source data collection steps: Leveraging the distributed ledger characteristics of blockchain technology, by setting up encrypted data collection points on the front-end pages, social sharing interfaces, and content creation modules of the e-commerce platform, multi-source data such as users' purchase behavior data, social recommendation data, content creation and interaction data on the e-commerce platform can be collected in real time and securely. Among them, purchase behavior data covers the types, quantities, amounts, frequency, purchase intervals, and return and exchange records of purchased products; social recommendation data includes the number of recommended new users, the purchase conversion rate of recommended users, and in-depth analysis of recommendation channels and recommendation paths; content creation and interaction data involves published product reviews, user experiences, the number of times participated in forum discussions, quality ratings, and indicators of the breadth and depth of content dissemination; the collected data is transmitted to blockchain nodes for storage using asymmetric encryption technology to ensure data security and non-tamperability;

[0009] S2. Intelligent stratification: Apply artificial intelligence algorithms to conduct in-depth analysis and weighted calculations on collected multi-source data. Use machine learning models to continuously optimize the user contribution evaluation model, automatically adjusting the weights of each data dimension based on market changes, changes in user behavior patterns, and industry trends to improve the accuracy and rationality of user stratification. Based on the preset user contribution evaluation model, intelligently stratify users into multiple tiers, such as ordinary users, advanced users, elite users, and expert users. Each tier corresponds to a different contribution value range, and the tier division takes into account the differences in user contributions in different business scenarios.

[0010] S3. Customized Strategy Development Steps: Develop differentiated buy-one-get-one-free and rebate strategies based on user tiers; dynamically adjust buy-one-get-one-free and rebate strategies based on market demand, product inventory, and product lifecycles; for popular products, provide exclusive limited-edition gifts and higher rebate percentages to high-tier users; for overstocked products, set up special promotional strategies for low-tier users, such as buy-one-get-one-free promotions and high rebates, to balance product sales and inventory management; and provide personalized upgrade tasks for users of different tiers to encourage them to upgrade their tiers.

[0011] S4. Real-time rebate and gift distribution steps: When a user successfully recommends a new user to purchase a product, the system triggers the rebate calculation in real time based on the blockchain smart contract. The rebate amount is determined based on factors such as the user's level, the purchase amount of the recommended user, the profit margin of the purchased product, and the quality of the user's recommendation history, such as the recommended user's long-term retention rate, repurchase rate, and activity level. The rebate amount is then distributed to the user's digital wallet through a secure encrypted payment channel. At the same time, for users who meet the gift redemption requirements, the system automatically records and arranges gift delivery. Gift information can be checked through blockchain traceability technology, including information on the entire process of gift production, warehousing, logistics, etc.

[0012] S5. Dynamic level update steps: re-collect and analyze user data according to the set time period, such as weekly or monthly, and dynamically adjust the user level according to the real-time changes in user contributions; the system generates a user contribution trend analysis report to provide users with a visual display of contribution data, helping users understand their own contributions and development direction on the platform; if the user contribution increases, the system automatically upgrades the user and pushes upgrade notifications and new rights and interests descriptions; if the user contribution decreases, the system lowers the user level accordingly and informs the user of the reasons and subsequent improvement suggestions, while considering the user's historical contribution data and special contribution situations for a comprehensive assessment.

[0013] On the basis of the above technical solution, the present invention can also be improved as follows.

[0014] Furthermore, in the multi-source data collection step, the data collection points have adaptive collection capabilities and can automatically adjust the collection frequency and accuracy according to changes in user behavior and fluctuations in data volume to ensure the integrity and validity of the data.

[0015] The beneficial effects of adopting the above further scheme are:

[0016] In the complex and ever-changing e-commerce environment, user behavior and data generation volumes are subject to constant change. The adaptive collection capabilities of data collection points can flexibly respond to these changes, avoiding information gaps caused by untimely or inaccurate data collection. For example, during promotional events, user purchasing and social sharing behavior can increase significantly. Adaptive collection capabilities can increase the frequency of collection to ensure comprehensive capture of user behavior data. When data volumes are relatively stable, automatic precision adjustment enables more detailed collection of key data, providing a solid and reliable data foundation for subsequent user analysis and strategy development. This overcomes the shortcomings of traditional data collection methods, which cannot adapt to dynamic data changes, and helps to gain more accurate insights into user value and behavior patterns.

[0017] Furthermore, in the intelligent stratification step, the artificial intelligence algorithm also integrates sentiment analysis technology to analyze the emotional tendencies of users in content creation and interaction, and uses it as a supplementary dimension for user contribution evaluation to further optimize the accuracy of user stratification.

[0018] The beneficial effects of adopting the above further scheme are:

[0019] Users' emotional tendencies in content creation and interaction can reflect their true attitudes toward the platform and products, as well as the depth of their engagement. By incorporating this dimension into user contribution evaluations through sentiment analysis technology, we can gain a more comprehensive understanding of users. For example, although a user's purchase amount is not high, they actively share positive product experiences in the platform community and have positive emotions, indicating a high degree of identification with the platform and potential influence. Sentiment analysis can be used to reasonably stratify them, avoiding stratification bias caused by relying solely on purchase data. This makes user stratification more precise, providing services that better suit the needs and values of users at different levels, improving user satisfaction and loyalty, and also helping the platform tap into potential high-quality users and opinion leaders, better leveraging user resources to drive platform development.

[0020] Furthermore, in the customized strategy formulation step, big data is used to analyze user preferences and behavior patterns to provide users with personalized product recommendations and gift combination suggestions, thereby improving users' purchasing intention and satisfaction.

[0021] The beneficial effects of adopting the above further scheme are:

[0022] Traditional buy-and-get-rebate strategies often lack specificity and fail to meet diverse user needs. Big data analysis enables personalized recommendations and gift combination suggestions, precisely matching user needs. For example, by analyzing a user's purchase and browsing history, relevant sports products and suitable gifts, such as backpacks and water bottles, can be recommended to outdoor enthusiasts. This personalized service can significantly increase user purchase intent, making them feel the platform's attention and value, thereby improving user satisfaction and loyalty. Furthermore, precise recommendations can increase product sales conversion rates, boost platform sales, and optimize platform operational efficiency.

[0023] Furthermore, in the real-time rebate and gift distribution steps, the calculation of the rebate amount also takes into account the market competition situation and the platform's marketing goals, and dynamically adjusts the rebate ratio to maintain the competitiveness and attractiveness of the platform.

[0024] The beneficial effects of adopting the above further scheme are:

[0025] The e-commerce market is highly competitive, and platforms' marketing objectives often adjust to changing market dynamics. Dynamically adjusting commission rebate ratios can help platforms maintain their competitive advantage. For example, when competitors launch similar commission rebate programs, platforms can appropriately increase the rebate ratio based on market conditions to attract more users to recommend and purchase products. Furthermore, by tailoring the rebate ratio to the platform's marketing objectives, such as promoting new products or clearing inventory, platforms can better achieve these goals. This helps platforms maintain their competitiveness, attract new users, retain existing users, and promote sustained business growth. It addresses the inflexibility and inability of traditional commission rebate strategies to adapt to market changes.

[0026] Furthermore, in the dynamic level update step, the system conducts limited association analysis with the user's social network, and with the user's authorization, explores potential recommendation opportunities and ways to improve user activity, providing the user with more targeted development suggestions.

[0027] The beneficial effects of adopting the above further scheme are:

[0028] A user's social network holds rich potential value and room for increasing user activity. Limited correlation analysis can uncover more recommendation opportunities. For example, if the system discovers that a user's social network contains many potential target customers, it can guide the user in making targeted recommendations, improving the conversion rate of recommendations. At the same time, it explores ways to increase user activity and provides personalized development suggestions, such as encouraging users to participate in specific social activities or create content to increase their contributions and activity on the platform. This not only helps users better participate in platform activities and enhance their own value, but also brings more traffic and business growth opportunities to the platform, optimizing the platform's user ecosystem.

[0029] A tiered cashback system based on user contributions, including:

[0030] Blockchain Data Collection and Storage Module: This module is responsible for collecting various user behavioral data throughout the e-commerce platform, leveraging blockchain encryption and distributed storage technologies to ensure data security and integrity, and storing the data in a blockchain ledger. This module is deeply integrated with the platform's various business systems, including order management, user management, social sharing, and content management systems, ensuring comprehensive and real-time data collection. Furthermore, it features data cleaning and pre-processing capabilities, automatically identifying and processing abnormal and duplicate data.

[0031] AI User Stratification Module: Based on artificial intelligence algorithms, this module analyzes and processes data provided by the blockchain data collection and storage module, stratifies users according to the user contribution evaluation model, and generates user tier information. Using deep learning algorithms, it continuously optimizes the user contribution evaluation model, improving the accuracy and stability of user stratification. Furthermore, this module possesses self-learning capabilities, automatically adjusting model parameters based on new data and user behavior patterns, and enabling differentiated analysis and stratification of user data across different business scenarios.

[0032] Strategy Development and Management Module: Develop differentiated buy-one-get-one-free and rebate strategies based on user tier information, and adjust strategies in real time based on market and product conditions to ensure their effectiveness and adaptability. Through big data analysis and market research, we continuously optimize strategy content to provide users with more attractive incentives.

[0033] Smart Contract Rebate and Gift Management Module: Leveraging blockchain smart contracts, this module automatically calculates and distributes rebates, as well as manages gift collection and redemption. Based on pre-set rules, the smart contract automatically executes corresponding actions when rebate and gift redemption conditions are met. Furthermore, it features intelligent early warning capabilities, enabling monitoring and warning of unusual rebate and gift redemption behaviors.

[0034] User Tier Dynamic Update Module: This module reacquires user data at predetermined intervals, updates user tier information, and notifies users of tier changes. It also archives and analyzes historical user contribution data to provide data support for policy adjustments. This module also interfaces with the user's personalized recommendation system to adjust recommendations based on changes in user tiers.

[0035] Visualization and Interaction Module: This module provides users and platform administrators with a visual data display interface. Users can view their contribution data, tier information, rebate records, and gift collection status. Platform administrators can monitor system operation status, user behavior data, and policy execution effectiveness to facilitate management decisions. Using data visualization technology, complex data is transformed into intuitive charts and graphs, making it easier for users and platform administrators to understand and analyze. This module also provides a user feedback interface to collect user opinions and suggestions for system optimization and improvement.

[0036] On the basis of the above technical solution, the present invention can also be improved as follows.

[0037] Furthermore, the blockchain data collection and storage module can interact with third-party data platforms to obtain external market data and industry data, providing a more comprehensive reference for user contribution evaluation.

[0038] The beneficial effects of adopting the above further scheme are:

[0039] Relying solely on a platform's own data makes it difficult to comprehensively assess user contributions and market trends. Interacting with third-party data platforms to obtain external data, such as industry reports and market research data, can provide a broader perspective for evaluating user contributions. For example, understanding the latest industry trends and competitor user behavior data can more accurately assess the value of user contributions within the overall market environment. This helps platforms develop more effective user segmentation and marketing strategies, enhance their competitiveness, avoid decision-making errors caused by information limitations, and provide strong support for their long-term development.

[0040] Furthermore, the AI user stratification module can work in collaboration with other artificial intelligence application modules, such as intelligent customer service and intelligent marketing, to achieve data sharing and functional complementarity, and enhance the overall intelligence level of the platform.

[0041] The beneficial effects of adopting the above further scheme are:

[0042] The AI user segmentation module works collaboratively with other AI application modules to achieve efficient data flow and optimized functional integration. For example, intelligent customer service can provide users with more accurate and personalized services based on user segmentation information, improving the efficiency and satisfaction of user inquiries. The intelligent marketing module utilizes user segmentation data to develop more targeted marketing campaigns and enhance marketing effectiveness. This collaborative working model enhances the overall intelligence level of the platform, providing users with a more convenient and efficient service experience, while also improving the platform's operational efficiency and management level, and promoting the comprehensive development of the platform's business.

[0043] Furthermore, the visualization display and interaction module has virtual reality and augmented reality display functions, providing users with a more immersive data display and interactive experience, especially in product display and gift preview, thereby enhancing user participation and purchasing desire.

[0044] The beneficial effects of adopting the above further scheme are:

[0045] Traditional product and gift presentation methods are relatively simple and difficult to attract users' attention. Virtual reality and augmented reality display features provide users with a new immersive experience. In terms of product display, users can use VR technology to view product details in all directions, such as trying on clothing and experiencing the placement of furniture in a virtual environment, enhancing their understanding and interest in the products. In terms of gift previews, users can more intuitively experience the appearance and performance of the gifts, increasing their anticipation and recognition of the gifts. This greatly increases user engagement and purchasing desire, helps improve product sales conversion rates and user satisfaction with gifts, adds new highlights to the platform's marketing activities, and enhances the platform's market competitiveness and user experience.

[0046] The present invention provides a method for providing a buy-and-give commission rebate based on user contributions and a tiered cashback system. This method has the following beneficial effects:

[0047] By establishing encrypted data collection points at multiple locations across e-commerce platforms, comprehensive multi-source data can be collected, providing a rich foundation for subsequent, accurate analysis of user contributions. Accurately understanding user purchasing preferences, social influence, and content creation capabilities can help platforms develop strategies that better meet user needs.

[0048] By utilizing blockchain technology and asymmetric encryption technology, we can ensure the security and non-tamperability of data during collection, transmission and storage, enhance users' trust in the platform, reduce the risk of data leakage, and facilitate the long-term stable operation of the platform.

[0049] The use of artificial intelligence algorithms and machine learning models to conduct in-depth weighted analysis of multi-source data and integrate sentiment analysis technology can more accurately divide user levels, so that users with different contributions can be reasonably distinguished and positioned, laying the foundation for precision marketing and personalized services.

[0050] Formulating dynamic buy-one-get-one-rebate strategies based on user levels, market demand, product inventory, and life cycle can not only improve the loyalty and consumption experience of high-level users, but also effectively clear out inventory backlogs, balance sales and inventory management, and motivate users to upgrade their levels through personalized upgrade tasks, thereby enhancing user stickiness and activity.

[0051] Based on blockchain smart contracts, real-time rebate calculation and distribution, as well as automatic recording and delivery of gifts, ensure efficient and transparent processes, reduce manual intervention, lower error rates and operating costs, and improve user experience.

[0052] The calculation of the rebate amount takes into account market competition and platform marketing goals and dynamically adjusts the rebate ratio, which helps maintain the platform's competitiveness and attractiveness, attracts more users to participate in recommendations, and promotes platform business growth.

[0053] The blockchain data collection and storage module interacts with third-party data platforms to obtain external data, providing a more comprehensive reference for user contribution evaluation, making the evaluation more objective and accurate, and the strategy formulation more forward-looking and adaptable.

[0054] The AI user stratification module works in conjunction with other artificial intelligence application modules to achieve data sharing and functional complementarity, improving the overall intelligence level of the platform. For example, intelligent customer service can provide more accurate services with the help of user stratification information, and intelligent marketing can formulate more effective marketing campaigns based on user contributions and preferences.

[0055] The VR and AR display functions of the visualization and interaction module provide users with immersive data display and interactive experience, stimulate users' interest and purchasing desire in product display and gift preview, improve user engagement and purchase conversion rate, and facilitate platform administrators to monitor and manage, thereby improving management decision-making efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings described herein are used to provide further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0057] In the attached figure:

[0058] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0059] Figure 2 This is a schematic diagram of the blockchain data collection and storage module workflow of the present invention;

[0060] Figure 3 This is a schematic diagram of the workflow of the AI user stratification module of the present invention;

[0061] Figure 4 A schematic diagram of the workflow of the customized strategy formulation module of the present invention;

[0062] Figure 5 This is a schematic diagram of the workflow of the real-time rebate and gift distribution module of the present invention;

[0063] Figure 6 This is a schematic diagram of the workflow of the user-level dynamic update module of the present invention;

[0064] Figure 7 This is a schematic diagram of the workflow of the visualization and interaction module of the present invention;

[0065] Figure 8 Schematic diagram of communication connections among modules of the present invention. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0067] See also Figures 1 to 8 As shown, the embodiments provided by the present invention are:

[0068] Example 1

[0069] Multi-source data collection and storage: On comprehensive e-commerce platform A, user Xiao Li is a digital product enthusiast and a lifestyle blogger. In the past month, Xiao Li purchased a new mobile phone, a pair of noise-canceling headphones, and a smartwatch. The platform, through encrypted data collection points deployed on the front-end page, collects detailed purchasing behavior data in real time, including the brand, model, price, and purchase date of the products purchased. It also records any returns or exchanges. Xiao Li shared his shopping experience on social media platforms and successfully recommended six friends to register and purchase products using a unique referral code. The platform's social sharing data collection module not only captures the number of new users referred, but also provides in-depth analysis of referral channels (such as WeChat Moments and Weibo) and the purchase conversion rate of the recommended users. Furthermore, Xiao Li posted three mobile phone reviews and two headphone user experiences on the platform's digital product forum. This content creation and interaction data is systematically collected, including article quality scores (based on a comprehensive assessment of likes, number of comments, and content depth) and content reach (such as article views and reposts). All collected data is securely transmitted to blockchain nodes using asymmetric encryption technology and stored in blockchain ledgers to ensure the data is tamper-proof and secure.

[0070] Intelligent Stratification: A uses artificial intelligence algorithms to conduct an in-depth analysis of Xiao Li's multi-source data. The machine learning model automatically adjusts the weights of data dimensions such as purchasing behavior, social recommendations, and content creation and interaction based on factors such as digital product sales trends, changes in user interest in digital products, and industry dynamics. For example, given the recent fierce competition in the digital product market, user social recommendations have become even more important for platform promotion, resulting in a higher weighting of social recommendation data. Through weighted calculations and based on a pre-set user contribution evaluation model, Xiao Li is classified as an elite user tier. This model fully considers Xiao Li's contributions in the digital product business context, such as the influence of his professional digital product reviews on other users' purchasing decisions.

[0071] Customized strategy development: Based on Xiao Li's elite user status, the platform has developed corresponding customized buy-one-get-one-free and rebate strategies. For popular digital products, such as a new high-performance laptop, Xiao Li can receive an exclusive limited-edition computer bag as a gift, and the rebate rate is increased to 15%. At the same time, the platform combines big data to analyze Xiao Li's preferences and behavior patterns, and recommends a smart bracelet that matches the brand of mobile phone he has already purchased, as well as a gift combination suitable for him (such as a brand-customized wireless charger and mobile phone holder). For a certain old model of mobile hard drive with an overstock, the platform launched a buy-one-get-one-free promotion strategy for ordinary users, while for elite users like Xiao Li, if they purchase, they will be given a higher rebate rate to promote inventory clearance. In addition, the platform has set personalized upgrade tasks for Xiao Li, such as inviting more users in the professional digital field to register and purchase products, so that he can be promoted to a higher level.

[0072] Real-time commission rebates and gift distribution: When Xiao Li successfully recommends a friend to purchase a laptop computer worth 5,000 yuan, the system triggers a real-time commission calculation based on a blockchain smart contract. The smart contract calculates a commission of 5,000 yuan x 15% = 750 yuan based on factors such as Xiao Li's elite user status, the friend's purchase amount, the laptop's profit margin (assuming 20%), and Xiao Li's recommendation history (the long-term retention rate of his recommended users is 60%, the repurchase rate is 30%, and his user activity is high). This commission rebate is then paid to Xiao Li's digital wallet in real time through a secure encrypted payment channel. Simultaneously, the system automatically records and arranges gift delivery for any products Xiao Li previously purchased that qualify for the gift. For example, the production, warehousing, and logistics process of the gift (customized headphone storage case) Xiao Li received with his mobile phone purchase can be tracked using blockchain traceability technology, allowing Xiao Li to clearly understand the gift's source and delivery progress.

[0073] Dynamic level update: The platform re-collects and analyzes Xiao Li's data on a monthly basis. As time goes by, Xiao Li's participation in platform activities has decreased due to his busy work, and the amount of purchases and the number of new users recommended have decreased. After the system re-evaluated, Xiao Li's contribution value has decreased, and his level has been adjusted to an advanced user. The platform promptly pushes notifications to Xiao Li, informing him of the reasons for the level change (such as a decrease in purchases and recommendations) and subsequent improvement suggestions (such as participating in digital product trial activities held by the platform and sharing experiences). At the same time, the system generates a user contribution trend analysis report for Xiao Li, which uses visual charts to show the changes in his contributions in purchases, recommendations, content creation, etc., to help Xiao Li understand his development on the platform.

[0074] Example 2

[0075] Multi-source data collection and storage: On maternal and infant e-commerce platform B, user Xiao Wang is a new mother. Within two months of her baby's birth, Xiao Wang purchased a large number of maternal and infant products, including infant formula, diapers, and strollers. The platform collects Xiao Wang's purchasing behavior data through encrypted data collection points on the front-end, social sharing interfaces, and content creation modules, including the types, quantities, and frequency of purchases, as well as any product changes. Xiao Wang shared her parenting experiences and shopping experiences on B on social platforms, successfully recommending eight new mothers to register and purchase products. The platform's social sharing data collection module captures the number of new users referred, the referral channels (primarily maternal and infant social groups), and the purchase conversion rate of the recommended users. Xiao Wang also posted five parenting articles and three product reviews in the platform's maternal and infant community. The system collects data on this content creation and interaction, including article quality ratings (based on recognition and likes from other mothers) and content dissemination (view counts and discussion activity within the maternal and infant community). All data is transmitted to blockchain nodes using asymmetric encryption and stored in the blockchain ledger to ensure data security and integrity.

[0076] Intelligent Stratification: Platform B uses artificial intelligence algorithms to analyze Xiao Wang's multi-source data. The machine learning model automatically adjusts the weights of various data dimensions based on factors such as seasonal changes in the maternal and infant market (such as seasonal variations in demand for baby clothing and supplies), emerging user demand trends for maternal and infant products (such as the growing interest in organic baby food), and industry dynamics (such as updates to quality standards for maternal and infant products). Through weighted calculations and a pre-set user contribution evaluation model, Xiao Wang is classified as an elite user. This model fully considers Xiao Wang's contributions to the maternal and infant business, such as the helpfulness of her shared parenting knowledge to other new mothers and her role in promoting platform products.

[0077] Customized Strategy Development: The platform has developed a customized strategy for Xiao Wang's elite user tier. For popular maternity and baby products, such as a new brand of infant safety seat, Xiao Wang receives an exclusive limited-edition baby toy gift with a 12% commission rebate. Furthermore, the platform analyzes Xiao Wang's preferences and behavior patterns through big data to recommend age-appropriate complementary foods and baby skincare products, as well as personalized gift packages (such as baby care sets and children's picture books). For an older model of crib with an overstock, the platform offers a high commission rebate (10%) for regular users, while elite users like Xiao Wang receive a complimentary crib mattress upon purchase. Furthermore, the platform provides Xiao Wang with personalized upgrade tasks, such as participating in platform-organized maternity and baby knowledge lectures and sharing insights, which can help him or her advance to a higher tier.

[0078] Real-time commission rebates and gift distribution: When Xiao Wang successfully recommends a friend to purchase a baby car seat worth 3,000 yuan, the system triggers a real-time commission calculation based on a blockchain smart contract. Based on factors such as Xiao Wang's elite user status, the friend's purchase amount, the car seat's profit margin (assuming 15%), and Xiao Wang's recommendation history (his recommended users have a long-term retention rate of 50% and a repurchase rate of 25%, indicating high activity), the commission is calculated as 3,000 yuan x 12% = 360 yuan. The commission is then paid to Xiao Wang's digital wallet via a secure encrypted payment channel. Furthermore, if Xiao Wang purchases eligible products, the system automatically arranges gift delivery. For example, the entire production, warehousing, and logistics process of the baby bottle cleaner Xiao Wang receives as a gift when purchasing milk powder can be tracked using blockchain traceability technology.

[0079] Dynamic Tier Updates: The platform recollects and analyzes Xiao Wang's data monthly. As her baby grows, Xiao Wang's needs for certain maternal and infant products change, adjusting her purchase frequency and amount, and fluctuating the number of new users she recommends. After the system reassessment, Xiao Wang's contribution value remains stable, maintaining her Elite user tier. The platform sends Xiao Wang a user contribution trend analysis report, visualizing her contributions in different areas with charts and graphs, and informing her of the ongoing benefits she will receive if she maintains her current level of activity. If Xiao Wang's contributions subsequently decline, the platform will lower her tier and provide corresponding improvement suggestions.

[0080] Example 3

[0081] Multi-source data collection and storage: On beauty e-commerce platform C, user Xiao Zhang is a beauty enthusiast and blogger. In the past month, Xiao Zhang purchased a variety of cosmetics, including lipstick, eye shadow, and liquid foundation. The platform collected Xiao Zhang's purchasing behavior data through encrypted data collection points, covering the brand, type, quantity, amount, purchase frequency, and any returns or exchanges. Xiao Zhang shared her makeup tutorials and experiences using platform products on social platforms, successfully recommending seven friends to register and purchase products. The platform's social sharing data collection module captured the number of new users referred, the referral channels (primarily beauty social platforms and short video platforms), and the purchase conversion rate of the recommended users. Xiao Zhang also posted four beauty product reviews and three makeup tips videos on the platform's beauty community. The system collected data on the creation and interaction of these content, including article and video quality ratings (based on likes, comments, and professionalism) and the breadth of content dissemination (number of views and shares across different platforms). All data was transmitted to blockchain nodes using asymmetric encryption and stored in the blockchain ledger, ensuring data security and immutability.

[0082] Intelligent Stratification: Platform C leverages artificial intelligence algorithms to conduct an in-depth analysis of Xiao Zhang's multi-source data. The machine learning model automatically adjusts the weights of various data dimensions based on factors such as beauty market trends (such as seasonally popular shades and makeup styles), changing user interest in the ingredients and efficacy of beauty products, and industry dynamics (such as new product launches and technological innovations by beauty brands). Using a weighted calculation and a pre-set user contribution evaluation model, Xiao Zhang is classified as an expert user. This model fully considers Xiao Zhang's professional contributions within the beauty business, such as the influence her expert reviews and tutorials have on other users' purchasing decisions.

[0083] Customized strategy development: Based on Xiao Zhang's expert user level, the platform developed a customized strategy. For popular beauty products, such as a brand's new limited-edition lipstick, Xiao Zhang can receive an exclusive high-end beauty tool set as a gift, with a commission rate of up to 20%. At the same time, the platform uses big data to analyze Xiao Zhang's preferences and behavior patterns, and recommends new cosmetics that match her makeup style and skin type, as well as personalized gift packages (such as brand-customized makeup bags and beauty eggs). For a certain old color lipstick with overstock, the platform launched a buy-two-get-one-free promotion strategy for ordinary users. For expert users like Xiao Zhang, if they purchase, they will be given a higher commission rate and priority to participate in new product trials. In addition, the platform has set personalized upgrade tasks for Xiao Zhang, such as inviting well-known beauty bloggers to join the platform and facilitate transactions, which can improve her level.

[0084] Real-time commission rebates and gift distribution: When Xiao Zhang successfully recommends a friend to purchase a new liquid foundation worth 800 yuan, the system triggers a real-time commission calculation based on a blockchain smart contract. Based on factors such as Xiao Zhang's expert user level, the friend's purchase amount, the foundation's profit margin (assuming 30%), and Xiao Zhang's recommendation history (the long-term retention rate of his recommended users is 70%, the repurchase rate is 40%, and his activity is high), the commission is calculated as 800 x 20% = 160 yuan. The commission is then distributed to Xiao Zhang's digital wallet via a secure encrypted payment channel. Simultaneously, if Xiao Zhang purchases eligible products, the system automatically arranges gift delivery. For example, the entire production, warehousing, and logistics process of the professional eye makeup brush Xiao Zhang receives as a gift when purchasing eye shadow can be tracked using blockchain traceability technology.

[0085] Dynamic Tier Updates: The platform recollects and analyzes Xiao Zhang's data monthly. With the rapidly evolving beauty market, Xiao Zhang continuously adjusts her content creation and recommendation strategies. After the system's reassessment, Xiao Zhang's contribution value has further increased, allowing her to maintain the Expert user tier and receive additional exclusive benefits. The platform sends Xiao Zhang a detailed user contribution trend analysis report, showcasing her outstanding contributions across various areas with visual charts. The platform also encourages her to continue leveraging her expertise and bring more value to the platform. If Xiao Zhang's contributions fluctuate in the future, the platform will adjust her tier accordingly and provide targeted recommendations.

[0086] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0087] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A buy-and-give-back commission method based on user contribution, characterized in that: The following steps are involved: S1. Multi-source data collection steps: Leveraging the distributed ledger characteristics of blockchain technology, by setting up encrypted data collection points on the front-end pages, social sharing interfaces, and content creation modules of the e-commerce platform, multi-source data such as users' purchase behavior data, social recommendation data, content creation and interaction data on the e-commerce platform can be collected in real time and securely. Among them, purchase behavior data covers the types, quantities, amounts, frequency, purchase intervals, and return and exchange records of purchased products; social recommendation data includes the number of recommended new users, the purchase conversion rate of recommended users, and in-depth analysis of recommendation channels and recommendation paths; content creation and interaction data involves published product reviews, user experiences, the number of times participated in forum discussions, quality ratings, and indicators of the breadth and depth of content dissemination; the collected data is transmitted to blockchain nodes for storage using asymmetric encryption technology to ensure data security and non-tamperability; S2. Intelligent stratification: Apply artificial intelligence algorithms to conduct in-depth analysis and weighted calculations on collected multi-source data. Use machine learning models to continuously optimize the user contribution evaluation model, automatically adjusting the weights of each data dimension based on market changes, changes in user behavior patterns, and industry trends to improve the accuracy and rationality of user stratification. Based on the preset user contribution evaluation model, intelligently stratify users into multiple tiers, such as ordinary users, advanced users, elite users, and expert users. Each tier corresponds to a different contribution value range, and the tier division takes into account the differences in user contributions in different business scenarios. S3. Customized Strategy Development Steps: Develop differentiated buy-one-get-one-free and rebate strategies based on user tiers; dynamically adjust buy-one-get-one-free and rebate strategies based on market demand, product inventory, and product lifecycles; for popular products, provide exclusive limited-edition gifts and higher rebate percentages to high-tier users; for overstocked products, set up special promotional strategies for low-tier users, such as buy-one-get-one-free promotions and high rebates, to balance product sales and inventory management; and provide personalized upgrade tasks for users of different tiers to encourage them to upgrade their tiers. S4. Real-time rebate and gift distribution steps: When a user successfully recommends a new user to purchase a product, the system triggers the rebate calculation in real time based on the blockchain smart contract. The rebate amount is determined based on factors such as the user's level, the purchase amount of the recommended user, the profit margin of the purchased product, and the quality of the user's recommendation history, such as the recommended user's long-term retention rate, repurchase rate, and activity level. The rebate amount is then distributed to the user's digital wallet through a secure encrypted payment channel. At the same time, for users who meet the gift redemption requirements, the system automatically records and arranges gift delivery. Gift information can be checked through blockchain traceability technology, including information on the entire process of gift production, warehousing, logistics, etc. S5. Dynamic level update steps: re-collect and analyze user data according to the set time period, such as weekly or monthly, and dynamically adjust the user level according to the real-time changes in user contributions; the system generates a user contribution trend analysis report to provide users with a visual display of contribution data, helping users understand their own contributions and development direction on the platform; if the user contribution increases, the system automatically upgrades the user and pushes upgrade notifications and new rights and interests descriptions; if the user contribution decreases, the system lowers the user level accordingly and informs the user of the reasons and subsequent improvement suggestions, while considering the user's historical contribution data and special contribution situations for a comprehensive assessment.

2. The method for providing a buy-and-give-back commission based on user contribution according to claim 1, characterized in that: In the multi-source data collection step, the data collection points have adaptive collection capabilities and can automatically adjust the collection frequency and accuracy according to changes in user behavior and fluctuations in data volume to ensure the integrity and validity of the data.

3. The method for providing a buy-and-give-back commission based on user contribution according to claim 1, characterized in that: In the intelligent stratification step, the artificial intelligence algorithm also integrates sentiment analysis technology to analyze users' emotional tendencies in content creation and interaction, and uses it as a supplementary dimension for user contribution evaluation to further optimize the accuracy of user stratification.

4. The method for providing a buy-and-give-back commission based on user contribution according to claim 1, characterized in that: In the customized strategy formulation step, big data is used to analyze user preferences and behavior patterns, provide users with personalized product recommendations and gift combination suggestions, and improve user purchasing intention and satisfaction.

5. The method for providing a buy-and-give-back commission based on user contribution according to claim 1, characterized in that: In the real-time rebate and gift distribution steps, the calculation of the rebate amount also takes into account the market competition situation and the platform's marketing goals, and dynamically adjusts the rebate ratio to maintain the platform's competitiveness and attractiveness.

6. The method for providing a buy-and-give-back commission based on user contribution according to claim 1, characterized in that: In the dynamic level update step, the system conducts limited association analysis with the user's social network, and with the user's authorization, explores potential recommendation opportunities and ways to improve user activity, providing the user with more targeted development suggestions.

7. A buy-and-give tiered cashback system based on user contribution, characterized in that: include: Blockchain Data Collection and Storage Module: This module is responsible for collecting various user behavioral data throughout the e-commerce platform, leveraging blockchain encryption and distributed storage technologies to ensure data security and integrity, and storing the data in a blockchain ledger. This module is deeply integrated with the platform's various business systems, including order management, user management, social sharing, and content management systems, ensuring comprehensive and real-time data collection. Furthermore, it features data cleaning and pre-processing capabilities, automatically identifying and processing abnormal and duplicate data. AI User Stratification Module: Based on artificial intelligence algorithms, this module analyzes and processes data provided by the blockchain data collection and storage module, stratifies users according to the user contribution evaluation model, and generates user tier information. Using deep learning algorithms, it continuously optimizes the user contribution evaluation model, improving the accuracy and stability of user stratification. Furthermore, this module possesses self-learning capabilities, automatically adjusting model parameters based on new data and user behavior patterns, and enabling differentiated analysis and stratification of user data across different business scenarios. Strategy Development and Management Module: Develop differentiated buy-one-get-one-free and rebate strategies based on user tier information, and adjust strategies in real time based on market and product conditions to ensure their effectiveness and adaptability. Through big data analysis and market research, we continuously optimize strategy content to provide users with more attractive incentives. Smart Contract Rebate and Gift Management Module: Leveraging blockchain smart contracts, this module automatically calculates and distributes rebates, as well as manages gift collection and redemption. Based on pre-set rules, the smart contract automatically executes corresponding actions when rebate and gift redemption conditions are met. Furthermore, it features intelligent early warning capabilities, enabling monitoring and warning of unusual rebate and gift redemption behaviors. User Tier Dynamic Update Module: This module reacquires user data at predetermined intervals, updates user tier information, and notifies users of tier changes. It also archives and analyzes historical user contribution data to provide data support for policy adjustments. This module also interfaces with the user's personalized recommendation system to adjust recommendations based on changes in user tiers. Visualization and Interaction Module: This module provides users and platform administrators with a visual data display interface. Users can view their contribution data, tier information, rebate records, and gift collection status. Platform administrators can monitor system operation status, user behavior data, and policy execution effectiveness to facilitate management decisions. Using data visualization technology, complex data is transformed into intuitive charts and graphs, making it easier for users and platform administrators to understand and analyze. This module also provides a user feedback interface to collect user opinions and suggestions for system optimization and improvement.

8. The user contribution-based buy-one-get-one-free tiered cashback system according to claim 7, characterized in that: The blockchain data collection and storage module can interact with third-party data platforms to obtain external market data and industry data, providing a more comprehensive reference for user contribution evaluation.

9. The user contribution-based buy-one-get-one-free tiered cashback system according to claim 7, characterized in that: The AI user stratification module can work in collaboration with other artificial intelligence application modules, such as intelligent customer service and intelligent marketing, to achieve data sharing and functional complementarity, and enhance the overall intelligence level of the platform.

10. The buy-one-get-one-free tiered cashback system based on user contribution according to claim 7, characterized in that: The visualization display and interaction module has virtual reality and augmented reality display functions, providing users with a more immersive data display and interactive experience, especially in product display and gift preview, thereby enhancing user participation and purchasing desire.

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