An artificial intelligence-based prepaid card management method and system

Abstract

The present application relates to the technical field of card management, and particularly relates to a prepayment card management method and system based on artificial intelligence, comprising the following steps: S1, user balance and consumption behavior data acquisition and feature analysis; S2, merchant preferential information collection and feature modeling; S3, user balance incentive rule intelligent generation; S4, merchant preferential and user demand matching optimization. The present application can predict the possible consumption intention of a user according to the specific consumption behavior of the user, and automatically trigger the incentive measures for balancing the use of the user balance at the appropriate time, optimizes the interaction between the merchant and the user, improves the acceptance and use frequency of the user to the merchant preferential, effectively increases the consumption conversion rate of the merchant, and further enhances the overall user experience of the prepayment card system.

Description

Technical Field

[0001] The present invention relates to the field of card management technology, and in particular to a prepaid card management method and system based on artificial intelligence. Background Art

[0002] With the increasing popularity of electronic payments and changes in consumer shopping behavior, prepaid cards (such as top-up cards and gift cards) have gradually become a common payment tool. Prepaid cards not only allow for purchases at merchants but also provide consumers with a way to pre-deposit funds, offering convenience, speed, and security. As a result, prepaid cards are increasingly being used in various scenarios, including e-commerce, retail, catering and entertainment, and transportation.

[0003] However, most current prepaid card management systems have limitations in intelligently matching user usage with merchant offers. Existing technologies primarily rely on simple functions such as balance inquiries, top-ups, and spending records, failing to fully leverage user behavior to intelligently enhance the consumer experience and optimize capital utilization efficiency. Furthermore, merchant offer recommendations are often based on fixed rules or historical user spending data, lacking a personalized recommendation mechanism. This results in a low user response rate to merchant offers. Specific issues include:

[0004] Idle balance problem: Many users have idle balances on their prepaid cards, which are not fully utilized, resulting in low liquidity, especially when users have low consumption frequency or expired balances.

[0005] Lack of personalized incentive mechanisms: Existing prepaid card management systems typically use unified incentives (such as fixed cash back or discounts) and fail to dynamically adjust based on factors such as users' consumption behavior patterns, consumption time, and merchant category preferences. This reduces the effectiveness of incentives and makes it difficult to stimulate users' consumption motivation.

[0006] Insufficient accuracy in merchant discount recommendations: Although merchant discount information has been used to a certain extent in many prepaid card systems, most discount recommendation systems do not consider the matching between users' personalized needs and merchants' discount conditions. The recommendation results are usually not accurate enough and cannot effectively improve users' acceptance and utilization of discounts. Summary of the Invention

[0007] The present invention provides a prepaid card management method and system based on artificial intelligence.

[0008] A prepaid card management method based on artificial intelligence, comprising the following steps:

[0009] S1, user balance and consumption behavior data collection and feature analysis:

[0010] Utilize the prepaid card management system to collect users' balance data, spending records, and frequently used merchant information in real time. Analyze the characteristics of users' historical spending behavior to generate personalized consumer behavior feature vectors. Features include balance usage patterns, spending time preferences, and merchant category preferences.

[0011] S2, merchant discount information collection and feature modeling:

[0012] Establish a merchant feature database in the prepaid card management system, collect dynamic merchant discount information in real time through the merchant interface, including discount amount, discount activity time window and applicable consumption conditions, use clustering algorithm to classify merchant discount information, and generate a merchant discount feature matrix;

[0013] S3, intelligent generation of user balance incentive rules:

[0014] Based on the user's consumption behavior feature vector, dynamic balance incentive rules are constructed. Dynamic balance incentive rules include incentive priority and incentive form. Incentive forms include additional balance cashback, discount coupons, or point rewards.

[0015] S4, Optimize the matching between merchant discounts and user needs:

[0016] Based on the merchant discount feature matrix, it matches the incentive form and gives priority to recommending discount information that is compatible with the balance incentive rules for matching and sorting merchant discount information.

[0017] Optionally, the S1 specifically includes:

[0018] S11, real-time data collection: Through the user data interface in the prepaid card management system, a connection is established with the user's consumption terminal (such as POS machine, online payment system) and the merchant's database. The collected data includes:

[0019] Balance data: user's current card balance and historical balance changes;

[0020] Consumption records: including consumption amount, payment method, and consumption time;

[0021] Frequently visited merchants: Based on consumption frequency, filter the merchants and merchant categories that users frequently visit.

[0022] S12, data preprocessing: normalize the collected raw data, organize the data by user dimension and store it in time series data format for subsequent model training;

[0023] S13, based on the collected consumer behavior data, generates a personalized consumer behavior feature vector, including:

[0024] Balance usage pattern: By analyzing the balance change trend, we can extract the volatility of users' balance usage and reflect their consumption habits;

[0025] Consumption time preference: Analyze the user's consumption time distribution characteristics and use time segments to analyze consumption probability;

[0026] Merchant category preference: Calculates the user's preference weight for merchants in each category based on the merchant category labels of frequently used merchant information in the user's consumption records.

[0027] Optionally, the balance usage pattern is extracted by analyzing the time series of changes in the user's balance (such as high-frequency consumption or long-term non-use), which is reflected as the balance volatility and calculated as:

[0028] Among them, ΔB is the balance volatility, which reflects the stability of the user's use of the balance, B u is the balance value after the u-th consumption, is the average balance value of the user during the observation period, N is the number of purchases during the observation period, and a higher balance volatility ΔB indicates that the user uses the balance more frequently or is more unstable; a lower balance volatility ΔB indicates that the user tends to maintain a stable balance.

[0029] The consumption time preference includes analyzing the user's consumption time distribution characteristics and using the time segment consumption probability to analyze: Among them, p t is the consumption probability of the user in the tth time period, n t is the number of times a user consumes in the tth time period, and T is the total number of time periods (for example, a day is divided into 24-hour periods, then is the total number of consumption times of the user in all time periods of a day, j is the time index, and the distribution p t Describe the user's consumption behavior preferences at different time periods of the day;

[0030] The merchant category preference includes weight calculation, which is calculated by counting the consumption frequency of different merchant categories in the user's consumption records: Among them, w i is the user’s preference weight for the i-th merchant, f i is the user's consumption frequency at the i-th merchant, q is the total number of merchant categories, f j represents the total consumption frequency of all merchant categories of the user, j index ranges from 1 to T (total number of time periods), and the preference weight w i It reflects the user's consumption tendency towards different merchant categories. The higher the value, the stronger the user's preference for merchants in that category.

[0031] Optionally, the consumption behavior feature vector integrates the features of each dimension into a user's personalized consumption behavior feature vector F, which is expressed as: Among them, ΔB is the balance usage pattern characteristic, is the merchant category preference feature, with dimension N (total number of merchant categories), It is the consumption time preference characteristic, and its dimension is T (total number of time periods).

[0032] Optionally, the merchant discount information collection and feature modeling in S2 specifically include:

[0033] S21, Establishing a Merchant Feature Database: In the prepaid card management system, basic merchant information and promotional information are collected in real time through the merchant interface to establish a merchant feature database. The merchant interface connects to the merchant system through the API to collect and store the following merchant data:

[0034] Merchant basic information: including merchant ID, merchant category, geographic location, and business hours;

[0035] Dynamic discount information: including discount amount, promotion time window, and applicable consumption conditions;

[0036] S22, clustering algorithm for feature classification: Use clustering algorithm to classify merchants' dynamic discount information to provide users with more accurate recommendations, including:

[0037] The K-means clustering algorithm is used to perform unsupervised learning on the merchant discount information and cluster them according to the following characteristics:

[0038] Discount amount d: The discount amount or discount ratio is used as the clustering dimension;

[0039] Promotion time window (t start ,t end ): The time period of the activity (such as holidays, weekends or weekdays) is used as the clustering dimension;

[0040] Applicable consumption conditions (c min ,c max ): The clustering dimension is based on consumption categories (such as catering, retail, entertainment, etc.).

[0041] The clustering results generate a merchant discount feature matrix, which includes the average discount rate, applicable time period, and target consumer group characteristics of each type of discount activity, making it easier to match appropriate discount information according to user needs.

[0042] Optionally, the K-means clustering algorithm classifies merchants according to their preferential features to form a merchant preferential feature matrix, and minimizes the Euclidean distance between merchants through iterative optimization. The goal is to minimize the cost function (loss function): Among them, K is the number of categories (cluster number) of merchant discount information, that is, how many different discount categories the merchant is divided into, N k is the number of merchants in the kth category, x i is the characteristic vector of the i-th merchant, including discount amount, preferential time window, consumption conditions, etc., μ k : The cluster center of the kth category (i.e., the average value of the characteristics of merchants in this category).

[0043] The process of Kmeans clustering algorithm is as follows:

[0044] 1. Initialization: Randomly select K merchants as the initial cluster centers;

[0045] 2. Assign clusters: For each merchant, calculate its distance to the center of each cluster and assign the merchant to the closest cluster;

[0046] 3. Update cluster center: Update the cluster center based on the average feature value of merchants in each cluster;

[0047] 4. Iteration: Repeat steps 2 and 3 until the cluster centers converge (i.e., the cluster centers no longer change);

[0048] After the cluster analysis is completed, each merchant will correspond to a discount feature vector, including the merchant's discount, discount activity time, and consumption conditions. The merchant discount feature matrix M after clustering is: M = [m1, m2, ..., m i ], where m i Represents the preferential feature vector of the i-th merchant. After normalization and clustering, the feature set represents the comprehensive characteristics of the merchant's preferential activities. This feature matrix serves as the input data for the subsequent optimization of matching merchant discounts with user needs.

[0049] Optionally, the incentive priority in S3 is calculated by sorting according to the comprehensive score G as follows:

[0050] Among them, α1, α2, α3 are the weight coefficients of each feature, and the incentive priority is obtained by sorting according to the comprehensive score G of each prepaid card user.

[0051] Optionally, the incentive form is formulated according to the consumption behavior feature vector F:

[0052] Additional balance cashback: When a user's balance is low in usage and high in balance, the incentive is to offer a balance cashback (e.g., return a certain percentage of the balance);

[0053] Discount coupons: When users frequently use prepaid cards for small purchases, the incentive is in the form of discount coupons (for example, discount coupons can be used when spending at specific merchants);

[0054] Points rewards: When users have diverse consumption categories or long-term consumption habits, the incentive form is points rewards (for example, points can be obtained for every certain amount of consumption, and points can be used to redeem goods or services).

[0055] Optionally, the optimization of matching merchant discounts with user needs in S4 specifically includes:

[0056] Matching additional balance cashback: Select matching cashback incentive offers, considering whether the merchant has discounts or promotions that are compatible with the balance cashback. For example, if a merchant offers offers like "cashback on purchases over a certain amount" or "cashback on purchases over a certain amount," these will be matched with the balance cashback incentive.

[0057] Matching discount coupons: Choose promotional activities that match discount coupons. Consider whether the merchant offers "specific discounts," "discount promotions," or "limited-time discounts." Discount coupon incentives are suitable for scenarios where users can get immediate price discounts when making purchases.

[0058] Matching point rewards: Choose promotional activities that match point rewards. Consider whether the merchant offers "points redemption" or "consumption accumulation points" activities. These merchant offers are usually related to the user's ongoing consumption habits.

[0059] Based on the matching results of the merchant discount feature matrix and the incentive form, a merchant discount matching list is generated, and the merchant discounts are sorted according to the matching degree.

[0060] An artificial intelligence-based prepaid card management system, used to implement the above-mentioned artificial intelligence-based prepaid card management method, includes the following modules:

[0061] Data collection module, used to collect users' balance data, consumption records and frequently used merchant information in real time;

[0062] The user feature analysis module analyzes the user's historical consumption behavior and generates a personalized consumption behavior feature vector. Features include balance usage patterns, consumption time preferences, and merchant category preferences.

[0063] The merchant discount information collection module collects dynamic discount information of merchants in real time through the merchant interface, including discount amount, discount activity time window and applicable consumption conditions, and stores the collected merchant discount information in the merchant feature database;

[0064] The merchant discount feature modeling module is used to classify merchant discount information using clustering algorithms and generate a merchant discount feature matrix;

[0065] Dynamic balance incentive rule generation module, which intelligently generates dynamic balance incentive rules based on user consumption behavior feature vectors. The incentive rules include incentive priority and incentive form;

[0066] The discount matching and recommendation module matches the merchant discount feature matrix with the user's dynamic balance incentive rules, gives priority to recommending discount information that is compatible with the balance incentive rules, and sorts the merchant discount information according to the recommendation results.

[0067] Beneficial effects of the present invention:

[0068] The present invention, by combining the user's personalized consumption behavior feature vector, identifies the user's consumption tendencies and needs, and realizes the intelligent generation and optimization of dynamic balance incentive rules. By deeply analyzing the user's balance usage pattern, consumption time preference and merchant category preference, it can predict the user's possible consumption intention based on the user's specific consumption behavior, and automatically trigger incentive measures to balance the user's balance usage at the appropriate time. In particular, when the balance is about to be idle or the consumption frequency decreases, dynamic incentives (such as cash back, points or discounts) can effectively stimulate user consumption, avoid long-term unused balances, and improve the liquidity of prepaid cards and the user's consumption activity.

[0069] The present invention improves the effectiveness of merchant discounts by establishing a merchant discount feature matrix and using intelligent recommendation to accurately match discount information with incentive measures. It combines incentive forms with merchant discounts and prioritizes recommending discount information that is compatible with dynamic balance incentive rules (for example, recommending cash back discounts to users with unused balances and recommending discount coupons to regular consumption users). This optimizes the interaction between merchants and users, improves users' acceptance of and frequency of use of merchant discounts, effectively increases merchants' consumption conversion rates, and further enhances the overall user experience of the prepaid card system. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0071] Figure 1 A schematic diagram of a prepaid card management method according to an embodiment of the present invention;

[0072] Figure 2 Schematic diagram of a prepaid card management system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0074] It should be noted that references in the specification to "one embodiment," "an embodiment," "an exemplary embodiment," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not every embodiment necessarily includes such specific features, structures, or characteristics. In addition, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0075] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0076] like Figure 1 As shown, a prepaid card management method based on artificial intelligence includes the following steps:

[0077] S1, user balance and consumption behavior data collection and feature analysis:

[0078] Utilize the prepaid card management system to collect users' balance data, spending records, and frequently used merchant information in real time. Analyze the characteristics of users' historical spending behavior to generate personalized consumer behavior feature vectors. Features include balance usage patterns, spending time preferences, and merchant category preferences.

[0079] S2, merchant discount information collection and feature modeling:

[0080] Establish a merchant feature database in the prepaid card management system, collect dynamic merchant discount information in real time through the merchant interface, including discount amount, discount activity time window and applicable consumption conditions, use clustering algorithm to classify merchant discount information, and generate a merchant discount feature matrix;

[0081] S3, intelligent generation of user balance incentive rules:

[0082] Based on the user's consumption behavior feature vector, dynamic balance incentive rules are constructed. Dynamic balance incentive rules include incentive priority and incentive form. Incentive forms include additional balance cashback, discount coupons, or point rewards.

[0083] S4, Optimize the matching between merchant discounts and user needs:

[0084] Based on the merchant discount feature matrix, it matches the incentive form and gives priority to recommending discount information that is compatible with the balance incentive rules for matching and sorting merchant discount information.

[0085] S1 specifically includes:

[0086] S11, real-time data collection: Through the user data interface in the prepaid card management system, a connection is established with the user's consumption terminal (such as POS machine, online payment system) and the merchant's database. The collected data includes:

[0087] Balance data: user's current card balance and historical balance changes;

[0088] Consumption records: including consumption amount, payment method, and consumption time;

[0089] Frequently visited merchants: Based on consumption frequency, filter the merchants and merchant categories that users frequently visit.

[0090] S12, data preprocessing: normalize the collected raw data, organize the data by user dimension and store it in time series data format for subsequent model training;

[0091] Normalization processing specifically includes: in,

[0092] x is the original data value, x′ is the normalized data value, and x min is the minimum value of the feature in the dataset, x max It is the maximum value of the feature in the data set. Normalization ensures that the data value is in the range of [0, 1], eliminating the impact of dimensional differences on subsequent models.

[0093] S13, based on the collected consumer behavior data, generates a personalized consumer behavior feature vector, including:

[0094] Balance usage pattern: By analyzing the balance change trend, we can extract the volatility of users' balance usage and reflect their consumption habits;

[0095] Consumption time preference: Analyze the user's consumption time distribution characteristics and use time segments to analyze consumption probability;

[0096] Merchant category preference: Calculates the user's preference weight for merchants in each category based on the merchant category labels of frequently used merchant information in the user's consumption records.

[0097] Balance usage patterns are analyzed over time to extract usage patterns (such as high-frequency consumption or long-term inactivity). The balance volatility is calculated as:

[0098] Among them, ΔB is the balance volatility, which reflects the stability of the user's use of the balance, B u is the balance value after the u-th consumption, is the average balance value of the user during the observation period, N is the number of purchases during the observation period, and a higher balance volatility ΔB indicates that the user uses the balance more frequently or is more unstable; a lower balance volatility ΔB indicates that the user tends to maintain a stable balance.

[0099] Consumption time preference involves analyzing the distribution characteristics of users' consumption time and using the time segment consumption probability to analyze: Among them, p t is the consumption probability of the user in the tth time period, n t is the number of times the user consumes in the tth time period, T is the total number of time periods (for example, if a day is divided into 24 hours, then T = 24), is the total number of consumption times of the user in all time periods of a day, j is the time index, and the distribution p t Describe the user's consumption behavior preferences at different time periods of the day;

[0100] Merchant category preference includes weight calculation. By counting the consumption frequency of different merchant categories in the user's consumption records, the merchant category preference weight is calculated: Among them, w i is the user’s preference weight for the i-th merchant, f i is the user's consumption frequency at the i-th merchant, q is the total number of merchant categories, represents the total consumption frequency of all merchant categories of the user, j index ranges from 1 to T (total number of time periods), and the preference weight w i It reflects the user's consumption tendency towards different merchant categories. The higher the value, the stronger the user's preference for merchants in that category.

[0101] The consumption behavior feature vector integrates the features of each dimension into the user's personalized consumption behavior feature vector F, which is expressed as: Among them, ΔB is the balance usage pattern characteristic, is the merchant category preference feature, with dimension N (total number of merchant categories), It is the consumption time preference characteristic, and its dimension is T (total number of time periods).

[0102] The merchant discount information collection and feature modeling in S2 specifically include:

[0103] S21, Establishing a Merchant Feature Database: In the prepaid card management system, basic merchant information and promotional information are collected in real time through the merchant interface to establish a merchant feature database. The merchant interface connects to the merchant system through the API to collect and store the following merchant data:

[0104] Merchant basic information: including merchant ID, merchant category, geographic location, and business hours;

[0105] Dynamic discount information: including discount amount, discount activity time window, and applicable consumption conditions. Specifically:

[0106] Discount amount: records the discount amount of the promotion (for example, 20 yuan off for purchases over 100 yuan, or 20% off);

[0107] Promotion time window: record the start and end time of promotions;

[0108] Applicable consumption conditions: including consumption amount, specific product or service categories, user type and other restrictions.

[0109] Using the merchant-side interface, the prepaid card management system pulls the latest discount information from the merchant system regularly or in real time to ensure that the discount information in the database is up to date. To avoid data conflicts, the system adopts a timestamp synchronization mechanism to ensure that relevant data will not be lost or missed when discount activities are updated.

[0110] S22, clustering algorithm for feature classification: Use clustering algorithm to classify merchants' dynamic discount information to provide users with more accurate recommendations, including:

[0111] The K-means clustering algorithm is used to perform unsupervised learning on the merchant discount information and cluster them according to the following characteristics:

[0112] Discount amount d: The discount amount or discount ratio is used as the clustering dimension;

[0113] Promotion time window (t start ,t end ): The time period of the activity (such as holidays, weekends or weekdays) is used as the clustering dimension;

[0114] Applicable consumption conditions (c min ,c max ): The clustering dimension is based on consumption categories (such as catering, retail, entertainment, etc.).

[0115] The clustering results generate a merchant discount feature matrix, which includes the average discount rate, applicable time period, and target consumer group characteristics of each type of discount activity, making it easier to match appropriate discount information according to user needs.

[0116] The K-means clustering algorithm classifies merchants according to their preferential features to form a merchant preferential feature matrix. Through iterative optimization, the Euclidean distance between merchants is minimized. The goal is to minimize the cost function (loss function): Among them, K is the number of categories (cluster number) of merchant discount information, that is, how many different discount categories the merchant is divided into, N k is the number of merchants in the kth category, x i is the characteristic vector of the i-th merchant, including discount amount, preferential time window, consumption conditions, etc., μ k : The cluster center of the kth category (i.e., the average value of the characteristics of merchants in this category).

[0117] The process of Kmeans clustering algorithm is as follows:

[0118] 1. Initialization: Randomly select K merchants as the initial cluster centers;

[0119] 2. Assign clusters: For each merchant, calculate its distance to the center of each cluster and assign the merchant to the closest cluster;

[0120] 3. Update cluster center: Update the cluster center based on the average feature value of merchants in each cluster;

[0121] 4. Iteration: Repeat steps 2 and 3 until the cluster centers converge (i.e., the cluster centers no longer change);

[0122] After the cluster analysis is completed, each merchant will correspond to a discount feature vector, including the merchant's discount, discount activity time, and consumption conditions. The merchant discount feature matrix M after clustering is: M = [m1, m2, ..., m i ], where m i Represents the preferential feature vector of the i-th merchant. The feature set after normalization and clustering represents the comprehensive characteristics of the merchant's preferential activities. This feature matrix serves as the input data for the subsequent optimization of matching merchant discounts with user needs.

[0123] m i Includes the following properties:

[0124] Discount amount: such as a percentage or a fixed amount of discount.

[0125] Promotion time window: such as the time period during which the promotion is valid (for example, weekends, holidays, etc.).

[0126] Applicable consumption conditions: such as whether there is a minimum consumption limit, whether it can be used in conjunction with other discounts, etc. The incentive priority in S3 is calculated based on the comprehensive score G:

[0127] Among them, α1, α2, and α3 are the weight coefficients of each feature, which combine balance usage patterns, merchant preferences, and time preferences to generate an incentive priority suitable for the user. The incentive priority is obtained by sorting according to the comprehensive score G of each prepaid card user (because the incentive pool quota is limited, the higher the incentive priority, the earlier the incentive is issued. When the incentive pool is used up, the incentive is closed. Different incentive quotas can also be set according to the incentive priority).

[0128] The incentive form is formulated according to the consumption behavior feature vector F:

[0129] Additional balance cashback: When a user's balance is low in usage and high in balance, the incentive is to offer a balance cashback (e.g., return a certain percentage of the balance);

[0130] Discount coupons: When users frequently use prepaid cards for small purchases, the incentive is in the form of discount coupons (for example, discount coupons can be used when spending at specific merchants);

[0131] Points rewards: When users have diverse consumption categories or long-term consumption habits, the incentive form is points rewards (for example, points can be obtained for every certain amount of consumption, and points can be used to redeem goods or services).

[0132] The optimization of matching merchant offers with user needs in S4 specifically includes:

[0133] Matching additional balance cashback: Select matching cashback incentive offers, considering whether the merchant has discounts or promotions that are compatible with the balance cashback. For example, if a merchant offers offers like "cashback on purchases over a certain amount" or "cashback on purchases over a certain amount," these will be matched with the balance cashback incentive.

[0134] Priority matching: The system prioritizes merchant offers that meet the "balance cashback" format, such as discounts and cashback, to ensure compatibility with the user's balance usage scenarios.

[0135] Matching discount coupons: Choose promotional activities that match discount coupons. Consider whether the merchant offers "specific discounts," "discount promotions," or "limited-time discounts." Discount coupon incentives are suitable for scenarios where users can get immediate price discounts when making purchases.

[0136] Priority matching: The system prioritizes merchant offers with clear discount conditions, such as "discount coupons" and "full-discount coupons," to increase the attractiveness of user consumption.

[0137] Matching point rewards: Choose promotional activities that match point rewards. Consider whether the merchant offers "points redemption" or "consumption accumulation points" activities. These merchant offers are usually related to the user's ongoing consumption habits.

[0138] Priority matching: The system prioritizes merchant offers that provide point rewards, such as "consumption points" or "accumulated consumption bonus points", to promote long-term user consumption and loyalty.

[0139] Based on the matching results of the merchant discount feature matrix and the incentive form, a merchant discount matching list is generated and the merchant discounts are sorted according to the matching degree. The sorting criteria may include:

[0140] Offers with high matching degree are recommended first: If the merchant offer is compatible with the current incentive form, it will be given higher priority.

[0141] Match incentive forms with user needs: Based on the user's consumption behavior analysis and dynamic balance incentive rules, merchant discounts that match the user's current needs (such as idle balance, consumption time, etc.) are recommended first.

[0142] Finally, the system-generated merchant discount matching list is sorted by priority and provided to users to ensure that users can obtain discount information that best matches their needs.

[0143] like Figure 2 As shown, an artificial intelligence-based prepaid card management system is used to implement the above-mentioned artificial intelligence-based prepaid card management method, including the following modules:

[0144] Data collection module, used to collect users' balance data, consumption records and frequently used merchant information in real time;

[0145] The user feature analysis module analyzes the user's historical consumption behavior and generates a personalized consumption behavior feature vector. Features include balance usage patterns, consumption time preferences, and merchant category preferences.

[0146] The merchant discount information collection module collects dynamic discount information of merchants in real time through the merchant interface, including discount amount, discount activity time window and applicable consumption conditions, and stores the collected merchant discount information in the merchant feature database;

[0147] The merchant discount feature modeling module is used to classify merchant discount information using clustering algorithms and generate a merchant discount feature matrix;

[0148] Dynamic balance incentive rule generation module, which intelligently generates dynamic balance incentive rules based on user consumption behavior feature vectors. The incentive rules include incentive priority and incentive form;

[0149] The discount matching and recommendation module matches the merchant discount feature matrix with the user's dynamic balance incentive rules, gives priority to recommending discount information that is compatible with the balance incentive rules, and sorts the merchant discount information according to the recommendation results.

[0150] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0151] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A prepaid card management method based on artificial intelligence, characterized in that: The following steps are involved: S1, user balance and consumption behavior data collection and feature analysis: Utilize the prepaid card management system to collect users' balance data, spending records, and frequently used merchant information in real time. Analyze the characteristics of users' historical spending behavior to generate personalized consumer behavior feature vectors. Features include balance usage patterns, spending time preferences, and merchant category preferences. S2, merchant discount information collection and feature modeling: Establish a merchant feature database in the prepaid card management system, collect dynamic merchant discount information in real time through the merchant interface, including discount amount, discount activity time window and applicable consumption conditions, use clustering algorithm to classify merchant discount information, and generate a merchant discount feature matrix; S3, intelligent generation of user balance incentive rules: Based on the user's consumption behavior feature vector, dynamic balance incentive rules are constructed. Dynamic balance incentive rules include incentive priorities and incentive forms, such as additional balance cashback, discount coupons, or point rewards. S4, Optimize the matching between merchant discounts and user needs: Based on the merchant discount feature matrix, the system matches the incentive form and prioritizes discount information that is compatible with the balance incentive rules. This information is then used to match and sort merchant discount information. The sorting criteria include: Highly matching offers are recommended first: if a merchant offer is compatible with the current incentive form, it will be given higher priority; Match incentives to user needs: Based on user consumption behavior analysis and dynamic balance incentive rules, we prioritize merchant offers that match users' current needs. The final generated merchant discount matching list is sorted by priority and provides discount information that best matches your needs.

2. The artificial intelligence-based prepaid card management method according to claim 1, characterized in that: Said S1 specifically includes: S11, real-time data collection: Through the user data interface in the prepaid card management system, a connection is established with the user consumption terminal and the merchant database. The collected data includes: Balance data: user's current card balance and historical balance changes; Consumption records: including consumption amount, payment method and consumption time; Frequently used merchant information: Based on consumption frequency, filter the merchants and merchant categories that users frequently visit; S12, data preprocessing: normalize the collected raw data, organize the data by user dimension and store it in time series data format; S13, based on the collected consumer behavior data, generates a personalized consumer behavior feature vector, including: Balance usage pattern: By analyzing the balance change trend, the volatility of users' balance usage is extracted; Consumption time preference: Analyze the user's consumption time distribution characteristics and use time segments to analyze consumption probability; Merchant category preference: Calculates the user's preference weight for merchants in each category based on the merchant category labels of frequently used merchant information in the user's consumption records.

3. The artificial intelligence-based prepaid card management method according to claim 2, characterized in that: The balance usage pattern is extracted by analyzing the time series of user balance changes, which is reflected in the balance volatility and calculated as: ,in, is the balance volatility, which reflects the stability of the user's use of the balance. It is The balance value after the first consumption, is the average balance value of the user during the observation period, is the number of consumptions during the observation period; The consumption time preference includes analyzing the user's consumption time distribution characteristics and using the time segment consumption probability to analyze: ,in, Is the user in The consumption probability of a time period, Is the user in The number of consumption times in a time period, is the total number of time periods, It is the total number of consumption times of the user in all time periods of a day. is the time index; The merchant category preference includes weight calculation, which is calculated by counting the consumption frequency of different merchant categories in the user's consumption records: ,in, Is the user's The preference weight of each merchant, Is the user in The consumption frequency of each merchant, is the total number of merchant categories, Indicates the total consumption frequency of the user across all merchant categories.

4. The artificial intelligence-based prepaid card management method according to claim 3, characterized in that: The consumption behavior feature vector is expressed as: ,in, is the balance usage pattern characteristic, is the merchant category preference feature, It is the consumption time preference characteristic.

5. The artificial intelligence-based prepaid card management method according to claim 1, characterized in that: The merchant preferential information collection and feature modeling in S2 specifically include: S21, Establishing a Merchant Feature Database: In the prepaid card management system, basic merchant information and promotional information are collected in real time through the merchant interface to establish a merchant feature database, collecting and storing the following merchant data: Merchant basic information: including merchant ID, merchant category, geographic location and business hours; Dynamic discount information: including discount amount, promotion time window and applicable consumption conditions; S22, clustering algorithm for feature classification: using clustering algorithm to classify the merchant's dynamic discount information, specifically including: The K-means clustering algorithm is used to perform unsupervised learning on the merchant discount information and cluster them according to the following characteristics: Discount amount : Use the discount amount or discount ratio as the clustering dimension; Promotion time window: The activity time period is used as the clustering dimension; Applicable consumption conditions The consumption category is used as the clustering dimension; The clustering results generate a merchant discount feature matrix, which includes the average discount rate, applicable time period and target consumer group characteristics of each type of discount activity.

6. The artificial intelligence-based prepaid card management method according to claim 5, characterized in that: The K-means clustering algorithm classifies merchants according to their preferential features to form a merchant preferential feature matrix. Through iterative optimization, the Euclidean distance between merchants is minimized. The goal is to minimize the cost function: ,in, is the number of categories of merchant discount information, that is, how many different discount categories the merchant is divided into. It is The number of merchants in the category, It is The characteristic vector of a merchant, : No. The cluster center of the class; After the cluster analysis is completed, each merchant will correspond to a discount feature vector, including the merchant's discount, discount activity time and consumption conditions. The merchant discount feature matrix after clustering is for: ,in, Indicates the The discount feature vector of each merchant.

7. The artificial intelligence-based prepaid card management method according to claim 4, characterized in that: The incentive priority in S3 is based on the comprehensive score The sort calculation is: ,in, is the weight coefficient of each feature, based on the comprehensive score of each prepaid card user Sort by incentive priority.

8. The artificial intelligence-based prepaid card management method according to claim 7, characterized in that: The incentive form is based on the consumption behavior feature vector Formulated: Additional Balance Cashback: When the user's balance is low in usage frequency and has a large balance, the incentive form is balance cashback; Discount coupons: When users frequently use prepaid cards for small purchases, the incentive is in the form of discount coupons; Points rewards: When users have diverse consumption categories or long-term consumption habits, the incentive form is points rewards.

9. The artificial intelligence-based prepaid card management method according to claim 1, characterized in that: The optimization of matching merchant discounts with user needs in S4 specifically includes: Matching of additional balance cashback: Select matching cashback incentive offers, considering whether the merchant has discounts or promotions that are compatible with the balance cashback. Matching discount coupons: Select promotions that match discount coupons, considering whether the merchant offers "discount offers", "discount promotions" or "limited-time discounts"; Matching point rewards: Choose promotions that match point rewards, considering whether the merchant offers "points redemption" or "points accumulation through consumption" activities; Based on the matching results of the merchant discount feature matrix and the incentive form, a merchant discount matching list is generated, and the merchant discounts are sorted according to the matching degree.

10. An artificial intelligence-based prepaid card management system, used to implement the artificial intelligence-based prepaid card management method according to any one of claims 1 to 9, characterized in that: Includes the following modules: Data collection module, used to collect users' balance data, consumption records and frequently used merchant information in real time; The user feature analysis module analyzes the user's historical consumption behavior and generates a personalized consumption behavior feature vector. Features include balance usage patterns, consumption time preferences, and merchant category preferences. The merchant discount information collection module collects dynamic discount information of merchants in real time through the merchant interface, including discount amount, discount activity time window and applicable consumption conditions, and stores the collected merchant discount information in the merchant feature database; The merchant discount feature modeling module is used to classify merchant discount information using clustering algorithms and generate a merchant discount feature matrix; Dynamic balance incentive rule generation module, which intelligently generates dynamic balance incentive rules based on user consumption behavior feature vectors. The incentive rules include incentive priority and incentive form; The discount matching and recommendation module matches the merchant discount feature matrix with the user's dynamic balance incentive rules, gives priority to recommending discount information that is compatible with the balance incentive rules, and sorts the merchant discount information according to the recommendation results.

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