Online payment method and related equipment based on aggregate payment system

Abstract

The present invention provides an online payment method based on an aggregated payment system, comprising: when a target user initiates payment, determining the amount to be paid and the category of goods to be paid for an order to be paid; if it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each available payment channel, and the number of categories of goods to be paid is greater than the dynamic category number threshold corresponding to each available payment channel, then based on the amount to be paid and the category of goods to be paid of the target user, determining a recommended payment channel from multiple available payment channels, each available payment channel corresponding to a dynamic payment amount threshold; when the target user determines that the final payment channel is the recommended payment channel, executing the target user's payment operation based on the recommended payment channel. The present invention can effectively avoid payment failures caused by insufficient thresholds or category mismatches for a single payment channel, reduce user operation complexity, shorten transaction processing time, and improve the smoothness of the payment process.

Description

Technical Field

[0001] The present invention relates to the field of digital payment technology, and in particular to an online payment method and related equipment based on an aggregated payment system. Background Art

[0002] With the rapid development of mobile payment technology, users can now make online payments through a variety of payment channels (such as bank cards, third-party payment platforms, and e-wallets) in their daily transactions. To improve payment convenience, aggregated payment systems have emerged, integrating multiple payment channels to provide users with a unified payment portal. However, existing aggregated payment systems still have certain limitations in practical applications: when a user initiates a payment, the system typically recommends a payment channel based on fixed rules or simple conditions, lacking dynamic adaptability to complex payment scenarios. For example, in scenarios involving high-value transactions or multiple product categories, existing systems may not effectively match the real-time risk control policies (such as dynamic payment amount thresholds and category restrictions) of the user's bound payment channel, resulting in increased payment failure rates or users having to manually switch payment methods multiple times, seriously affecting payment efficiency and user experience. Furthermore, existing technologies lack intelligent decision-making mechanisms for both payment amount and product category, making it difficult to optimize payment success rates while ensuring transaction security. Therefore, a solution that can dynamically assess the compatibility of payment channels and intelligently recommend the optimal payment strategy based on multi-dimensional conditions is urgently needed to address these technical limitations. Summary of the Invention

[0003] An embodiment of the present invention provides an online payment method based on an aggregated payment system, aiming to provide a solution that can dynamically evaluate the adaptability of payment channels and intelligently recommend the optimal payment strategy based on multi-dimensional conditions. By performing multi-dimensional evaluations based on dynamic thresholds and product categories, the dynamic payment strategies of each payment channel are matched in real time, effectively avoiding payment failures caused by insufficient thresholds or category mismatches in a single payment channel, reducing the number of times users manually switch payment methods, and shortening transaction processing time. This can avoid the waste of resources caused by frequently trying different payment methods, while reducing the complexity of user operations and improving the smoothness and satisfaction of the payment process.

[0004] In a first aspect, an embodiment of the present invention provides an online payment method based on an aggregated payment system, wherein the aggregated payment system has multiple available payment channels, each of which is a payment channel that has been bound and verified by a user. The method includes:

[0005] When the target user initiates payment, the amount to be paid and the category of goods to be paid for the order to be paid are determined;

[0006] If it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels, and the number of commodity categories to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, then based on the amount to be paid by the target user and the commodity categories to be paid, a recommended payment channel is determined from the multiple available payment channels, each of the available payment channels corresponding to a dynamic payment amount threshold;

[0007] When the target user determines that the final payment channel is the recommended payment channel, the payment operation of the target user is performed based on the recommended payment channel.

[0008] Optionally, before the step of determining that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels, and the number of commodity categories to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, the method further includes:

[0009] Obtaining the target user's historical payment information, wherein the historical payment information includes historical payment amounts, historical payment channels, and historical payment commodity categories;

[0010] Based on the historical payment amount, the historical payment channel and the historical payment commodity category, a dynamic payment amount threshold and a dynamic category quantity threshold corresponding to each of the available payment channels are determined.

[0011] Optionally, the step of determining a dynamic payment amount threshold and a dynamic category quantity threshold corresponding to each of the available payment channels based on the historical payment amount, the historical payment channel, and the historical payment commodity category specifically includes:

[0012] For the historical payment channels, determining the historical payment amount set and the historical payment commodity category set corresponding to each available channel;

[0013] Performing quantile regression calculation on the historical payment amount set to obtain a dynamic payment amount threshold corresponding to the available payment channel;

[0014] An association calculation of commodity categories is performed on the historical payment commodity category set to obtain a dynamic category quantity threshold corresponding to the available payment channel.

[0015] Optionally, the step of performing quantile regression calculation on the historical payment amount set to obtain a dynamic payment amount threshold corresponding to the available payment channel includes:

[0016] Determining a payment amount corresponding to a target percentile based on historical payment amounts in the historical payment amount set;

[0017] Determine the transaction activity weight, the total historical transaction frequency, and the average daily transaction frequency based on the historical payment time of the historical payment amount concentration;

[0018] Obtaining a channel reliability coefficient and a number of successful transactions of the available payment channel;

[0019] Based on the payment amount corresponding to the target percentile, the transaction activity weight, the total historical transaction frequency, the average daily transaction frequency, the channel reliability coefficient and the number of successful transactions of the channel, a dynamic payment amount threshold corresponding to the available payment channel is determined.

[0020] Optionally, after the step of determining the dynamic payment amount threshold corresponding to the available payment channel, the method further includes:

[0021] When the current time period is reached, the channel reliability coefficient is updated according to the average daily transaction frequency to obtain the channel reliability coefficient corresponding to the current time period;

[0022] When a fluctuation event is detected in the available payment channel during the current time period, the dynamic payment amount threshold is compensated and calculated using the channel reliability coefficient corresponding to the current time period to obtain a compensated dynamic payment amount threshold.

[0023] Optionally, the step of performing association calculation on the historical payment commodity category set to obtain a dynamic category quantity threshold corresponding to the available payment channel specifically includes:

[0024] determining a transaction preference level for each commodity category based on a transaction success rate for each commodity category in the historical payment commodity category set;

[0025] Determining a commodity category association weight matrix based on the transaction preference levels of the commodity categories;

[0026] Based on the commodity category association weight matrix, a dynamic category quantity threshold corresponding to the available payment channel is determined.

[0027] Optionally, the step of determining a recommended payment channel from the plurality of available payment channels based on the amount to be paid by the target user and the category of the goods to be paid specifically includes:

[0028] Based on the amount to be paid and the category of the goods to be paid, combined with the dynamic payment amount threshold corresponding to each of the available payment channels and the dynamic category quantity threshold corresponding to each of the available payment channels, the order to be paid is split to obtain multiple sub-orders to be paid;

[0029] Based on the multiple sub-orders to be paid, a recommended payment channel is determined from the multiple available payment channels.

[0030] In a second aspect, an embodiment of the present invention further provides an online payment device based on an aggregated payment system, wherein the aggregated payment system has multiple available payment channels, each of which is a payment channel that has been bound and verified by a user, and the online payment device based on the aggregated payment system includes:

[0031] The first processing module is used to determine the amount to be paid and the category of goods to be paid for the order to be paid when the target user initiates payment;

[0032] a second processing module configured to, if it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels and the number of commodity categories to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, determine a recommended payment channel from the plurality of available payment channels based on the amount to be paid by the target user and the commodity categories to be paid, each of the available payment channels corresponding to a dynamic payment amount threshold;

[0033] The payment module is configured to execute a payment operation of the target user based on the recommended payment channel when the target user determines that the final payment channel is the recommended payment channel.

[0034] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the online payment method based on the aggregated payment system provided in an embodiment of the present invention are implemented.

[0035] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the online payment method based on the aggregated payment system provided in the embodiment of the invention are implemented.

[0036] In an embodiment of the present invention, when a target user initiates a payment, the amount to be paid and the category of goods to be paid for the order to be paid are determined; if it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels, and the number of the categories of goods to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, then based on the amount to be paid and the category of goods to be paid of the target user, a recommended payment channel is determined among the multiple available payment channels, and each of the available payment channels corresponds to a dynamic payment amount threshold; when the target user determines that the final payment channel is the recommended payment channel, the payment operation of the target user is executed based on the recommended payment channel. The present invention performs multi-dimensional evaluation through dynamic thresholds and product categories, and matches the dynamic payment strategies of each payment channel in real time, effectively avoiding payment failures caused by insufficient thresholds of a single payment channel or category mismatches, reducing the number of times users manually switch payment methods, and shortening transaction processing time; it can avoid the waste of resources caused by frequent attempts at different payment methods, while reducing the complexity of user operations and improving the fluency and satisfaction of the payment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a flow chart of an online payment method based on an aggregated payment system provided by an embodiment of the present invention;

[0039] Figure 2 This is a schematic structural diagram of an online payment device based on an aggregated payment system provided by an embodiment of the present invention;

[0040] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0041] 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] like Figure 1 As shown, Figure 1This is a flowchart of an online payment method based on an aggregated payment system provided by an embodiment of the present invention. The aggregated payment system has multiple available payment channels, each of which is a payment channel that has been bound and verified by the user. The online payment method based on the aggregated payment system includes the following steps:

[0043] 101. When the target user initiates payment, the amount to be paid and the category of the goods to be paid for the order to be paid are determined;

[0044] 102. If it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels, and the number of commodity categories to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, then based on the amount to be paid by the target user and the commodity categories to be paid, a recommended payment channel is determined from the plurality of available payment channels, each of the available payment channels corresponding to a dynamic payment amount threshold;

[0045] 103. When the target user determines that the final payment channel is the recommended payment channel, perform a payment operation of the target user based on the recommended payment channel.

[0046] In an embodiment of the invention, when a target user initiates a payment request through the aggregated payment system, the system obtains an order to be paid, which includes an amount to be paid and a category of goods to be paid.

[0047] The amount to be paid is the actual payment after parsing the total price, shipping costs, and discounts through the order transaction interface. The category of the item to be paid is based on the classification identifier in the item database (such as food, electronics, virtual services, etc.). The category tags of all items in the order can be extracted and the quantity of each category can be counted.

[0048] The system can obtain real-time dynamic threshold data of all available payment channels that the user has bound from the pre-configured payment channel management module. The real-time dynamic threshold data includes: dynamic payment amount threshold and dynamic category quantity threshold.

[0049] Dynamic payment amount thresholds can be understood as being calculated dynamically based on each payment channel's risk control strategies, such as single transaction limits, daily cumulative limits, user preferred amounts (small, medium, and large payments), historical user transactions, and the current time period's risk level. Dynamic category quantity thresholds can be understood as being generated based on payment channel restrictions on product categories (e.g., limits on the quantity of virtual goods and cross-border goods).

[0050] If the amount to be paid exceeds the dynamic payment amount threshold for all available payment channels, and the total number of product categories to be paid exceeds the dynamic category number threshold for all available payment channels, the recommended payment channel calculation module is triggered to recommend available payment channels. If either condition is not met, a payment channel is selected based on default rules (e.g., user default, historical usage frequency).

[0051] Specifically, the amount to be paid is compared with the dynamic payment amount thresholds of all available payment channels. If, among all available payment channels, there is no available payment channel whose dynamic payment amount threshold is smaller than the amount to be paid, it can be determined that the amount to be paid exceeds the dynamic payment amount threshold of all available payment channels. If there is any available payment channel whose dynamic payment amount threshold is smaller than or equal to the amount to be paid, it can be determined that the amount to be paid does not exceed the dynamic payment amount threshold of all available payment channels.

[0052] The total number of commodity categories to be paid is compared with the dynamic category quantity threshold of all available payment channels. If, among all available payment channels, there is no available payment channel whose dynamic category quantity threshold is less than the total number of commodity categories to be paid, it can be determined that the total number of commodity categories to be paid exceeds the dynamic category quantity threshold of all available payment channels. If there is any available payment channel whose total number of commodity categories to be paid is less than or equal to the amount to be paid, it can be determined that the total number of commodity categories to be paid does not exceed the dynamic category quantity threshold of all available payment channels.

[0053] The weight coefficient of each available payment channel can be set based on the payment amount adaptability (the ratio of the amount to be paid to the remaining available balance of the available payment channel) and the product category matching (the overlap between the order category and the category allowed by the available payment channel); the top N available payment channels with the largest to smallest weight coefficients are used as recommended payment channels.

[0054] In a possible embodiment, available payment channels are sorted in descending order according to their comprehensive scores, and the channels with the highest scores are preferentially selected as recommended payment channels; the comprehensive scores include the frequency of use of available payment channels, the frequency of manual selection of available payment channels, and the preferential strength of available payment channels (such as subsidies for dedicated payment channels).

[0055] In a possible embodiment, if the recommended payment channel is unavailable due to external reasons (such as temporary maintenance of the bank system), it will automatically switch to the suboptimal channel and update the recommendation result.

[0056] In one possible embodiment, for high-value multi-category orders, if automatic payment splitting is allowed in the target user's account settings, a splitting plan can be dynamically generated based on order characteristics and payment channel restrictions, including three modes: splitting by amount ratio, splitting by product category grouping, and mixed splitting.

[0057] By splitting a high-value multi-category order into multiple sub-orders, multiple available payment channels are recommended to pay the multiple sub-orders. When the target user determines that the final payment channel is the recommended payment channel, the payment operations of the multiple sub-orders are executed based on the recommended payment channel.

[0058] In an embodiment of the present invention, when a target user initiates a payment, the amount to be paid and the category of goods to be paid for the order to be paid are determined; if it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels, and the number of the categories of goods to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, then based on the amount to be paid and the category of goods to be paid of the target user, a recommended payment channel is determined among the multiple available payment channels, and each of the available payment channels corresponds to a dynamic payment amount threshold; when the target user determines that the final payment channel is the recommended payment channel, the payment operation of the target user is executed based on the recommended payment channel. The present invention performs multi-dimensional evaluation through dynamic thresholds and product categories, and matches the dynamic payment strategies of each payment channel in real time, effectively avoiding payment failures caused by insufficient thresholds of a single payment channel or category mismatches, reducing the number of times users manually switch payment methods, and shortening transaction processing time; it can avoid the waste of resources caused by frequent attempts at different payment methods, while reducing the complexity of user operations and improving the fluency and satisfaction of the payment process.

[0059] It is understandable that in the specific implementation of this application, related data such as user data, payment data, channel data, order data, behavioral data, etc. are involved. When the embodiments in this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0060] Optionally, before the step of determining that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels, and the number of commodity categories to be paid is greater than the dynamic category quantity threshold corresponding to each of the available payment channels, the method further includes: obtaining historical payment information of the target user, the historical payment information including historical payment amounts, historical payment channels, and historical payment commodity categories; and determining the dynamic payment amount threshold and dynamic category quantity threshold corresponding to each of the available payment channels based on the historical payment amounts, the historical payment channels, and the historical payment commodity categories.

[0061] In an embodiment of the present invention, historical payment information of the target user may be extracted from a payment log database.

[0062] The historical payment amount can be the payment amount of each transaction of the user. The single transaction amount, average daily transaction amount and monthly cumulative transaction amount completed by the user through each payment channel can be counted based on the historical payment amount.

[0063] The historical payment channel may be the payment channel used for each transaction of the user, and statistics on the usage frequency, most recent usage time, success rate, etc. of each payment channel may be collected based on the historical payment channel.

[0064] The historical payment commodity categories may be the commodity categories included in each transaction, and the classification data of related order commodities, the commodity categories and quantity distribution paid by users through various payment channels, etc. may be counted based on the historical payment commodity categories.

[0065] After obtaining historical payment information, data preprocessing can be performed on the historical payment information. Data preprocessing can include removing abnormal data (such as test transactions and refund orders); desensitizing sensitive information (such as user ID and bank card number) and encoding it only using system identifiers; and sliding statistics according to a preset time window (such as the last 30 days) to ensure data timeliness.

[0066] Based on historical payment information, dynamic thresholds for each payment channel can be generated through a rule engine and machine learning models.

[0067] Specifically, the dynamic payment amount threshold can be calculated based on the payment channel's preset fixed risk control limit (e.g., a bank-mandated maximum of 50,000 yuan per transaction). This is combined with a user behavior modifier. For example, if a user's maximum successful payment amount through the payment channel is M, and their recent success rate (e.g., within 7 days) is ≥95%, the dynamic threshold is raised to min(basic threshold, M × safety factor), where the safety factor is 1.1-1.3. If the user has recently experienced payment failures (e.g., insufficient balance, risk control interception), the dynamic threshold is lowered to base threshold × decay factor, with the decay factor decreasing in steps of 0.9, 0.8, etc. based on the number of failures. For example, if user B's maximum successful payment amount through payment channel P is 8,000 yuan, the base threshold is 10,000 yuan, and the safety factor is 1.2, then the dynamic payment amount threshold is min(10,000, 8,000 × 1.2) = 9,600 yuan.

[0068] To calculate the dynamic category threshold, we can factor in the payment channel's hard restrictions on product categories (e.g., prohibiting virtual goods and limiting cross-border goods to no more than two categories). We then calculate the distribution of product categories successfully paid for by a user through that payment channel. If a user frequently pays for orders across multiple categories (e.g., an average of three or more categories per month) and has no risk control record, the dynamic threshold is increased to the initial threshold + an elastic increment (e.g., +1 category). If a user has recently been blocked for exceeding the category limit, the dynamic threshold is reduced to the initial threshold - a penalty reduction (e.g., -1 category). For example, if payment channel Q has an initial category threshold of 2, and user C successfully pays for five orders in three categories through Q in the past 30 days, the dynamic category threshold is adjusted to 3 categories.

[0069] The calculated dynamic threshold is written to the cache (such as Redis) to ensure millisecond-level reading; upon receiving a policy change notification from the payment channel (such as a temporary limit adjustment by the bank or an active limit adjustment by the user), the threshold is triggered to be recalculated.

[0070] For example, user D is linked to payment channels R (with a basic threshold of 5,000 RMB) and S (with a basic threshold of 8,000 RMB). His historical payment behavior is as follows: He successfully paid for six orders through channel R, with amounts ranging from 3,000 to 6,000 RMB (exceeding channel R's basic threshold but not blocked by the bank). However, when paying through channel S, he was blocked twice by risk control due to frequent payments exceeding 8,000 RMB. The dynamic threshold for channel R is: min(5,000, 6,000 × 1.2) = 6,000 RMB (due to historical successful payments of 6,000 RMB without failures); the dynamic threshold for channel S is: 8,000 × 0.8 = 6,400 RMB (due to a decrease triggered by recent failures). When user D initiates a payment of 7,000 yuan, the system automatically excludes channel S (threshold 6,400 yuan) and only recommends channel R (threshold 6,000 yuan). If the user accepts the excess (7,000-6,000=1,000 yuan) and makes up the difference with the balance, the payment is completed.

[0071] In some possible embodiments, a random forest or neural network model can also be trained, with input features including historical amounts, categories, payment times, user credit scores, etc., to output dynamic threshold adjustment suggestions; the model is regularly trained offline, and the threshold is corrected in real time during online inference.

[0072] In some possible embodiments, a time decay weight (such as exponential decay) may be assigned to the historical data so that recent transactions have a greater impact on the threshold.

[0073] In some possible embodiments, when the historical payment information of the target user is relatively sparse, the dynamic threshold may be initialized with reference to the historical payment behaviors of similar user groups.

[0074] The present invention breaks through the fixed limitations of payment channels through personalized threshold adaptation, dynamically relaxes or tightens the threshold based on the user's actual payment ability, and improves payment flexibility; it automatically adjusts the threshold based on the user's historical behavior, thereby improving the success rate while preventing the risk of malicious cash withdrawal or money laundering.

[0075] Optionally, the step of determining the dynamic payment amount threshold and the dynamic category quantity threshold corresponding to each of the available payment channels based on the historical payment amount, the historical payment channel and the historical payment commodity category specifically includes: for the historical payment channel, determining the historical payment amount set and the historical payment commodity category set corresponding to each of the available channels; performing quantile regression calculation on the historical payment amount set to obtain the dynamic payment amount threshold corresponding to the available payment channel; performing commodity category association calculation on the historical payment commodity category set to obtain the dynamic category quantity threshold corresponding to the available payment channel.

[0076] In this embodiment of the present invention, for each available payment channel, the historical payment amount set and historical payment product category set corresponding to that channel are extracted from the historical payment information. The historical payment amount set is the set of amounts for all orders successfully paid by the user through that channel; the historical payment product category set is the set of product category tags included in the orders paid by the user through that channel. For example, the historical amount set for payment channel K is {3,000 yuan, 5,000 yuan, 7,000 yuan}, and the category set is {electronic products, home appliances, cross-border goods}.

[0077] Set the target quantile Q based on the risk tolerance of the payment channel p , Q p The value range of τ is [0.85-1], which means that 85%-95% of the historical successful transaction amounts are covered. If the channel needs to allow most normal transactions but intercept transactions with extremely high failure risks, then select τ=0.99.

[0078] Establish a quantile regression model with time, channel score, and transaction scenario as independent variables and historical payment amount as dependent variable. Then Q p (Amount | X) = β0 + β1*Time + β2*Credit Score + β3*Scenario Coefficient. Use gradient descent to solve for parameter β, minimize the quantile loss function, input the current transaction features (such as time = "holiday", credit score = 750, scenario = "big sale"), and calculate the predicted quantile value Q. p ; Combined with the fixed risk control limit of the payment channel, the dynamic payment amount threshold is taken as min(Q p , fixed limit).

[0079] The Apriori algorithm or FP-Growth algorithm can be used to extract frequent itemsets and association rules from historical payment product categories. For example, if "electronic products" and "accessories" often appear together (support = 30%), the frequent itemset {electronic products, accessories} is formed. The maximum number of categories N_max in the historical product category set that users successfully paid for through this channel is extracted. If the association confidence of a category exceeds a threshold (e.g., 70%), it is merged into a logical "super category." The dynamic category threshold is N_max × the merging coefficient (merging coefficient ∈ [0.5, 1]; the stronger the correlation, the smaller the coefficient). Given the historical maximum number of categories N_max = 4 and the merging coefficient = 0.75 (due to the presence of two strongly associated categories), the dynamic threshold is 4 × 0.75 = 3 categories.

[0080] It should be noted that if the payment channel has strict restrictions on specific categories (such as virtual goods), the category quantity threshold for orders of this type will be enforced to be 1.

[0081] Optionally, the step of performing quantile regression calculation on the historical payment amount set to obtain the dynamic payment amount threshold corresponding to the available payment channel includes: determining the payment amount corresponding to the target percentile based on the historical payment amount in the historical payment amount set; determining the transaction activity weight, the historical total transaction frequency and the average daily transaction frequency based on the historical payment time in the historical payment amount set; obtaining the channel reliability coefficient of the available payment channel and the number of successful transactions of the channel; determining the dynamic payment amount threshold corresponding to the available payment channel based on the payment amount corresponding to the target percentile, the transaction activity weight, the historical total transaction frequency, the average daily transaction frequency, the channel reliability coefficient and the number of successful transactions of the channel.

[0082] In the embodiment of the present invention, the channel historical payment amount dataset M={m1,m2,...,m n}, perform sliding window quantile calculation and obtain the basic payment threshold as:

[0083] Th base =Q min (M [t−w:t]+ )+H⋅Q max (M [t−w:t]+ )

[0084] Among them, Th base is the basic payment threshold, Q min is the minimum quantile, Q max is the maximum quantile, t represents the tth historical payment information in the historical payment amount dataset, M t−w:t represents the [t−w:t]th +The historical payment amount corresponding to the historical payment information, w is the time window, [ ] + Indicates rounding up, H is the empirical coefficient, and the value range of H is [0.10, 0.40].

[0085] Calculation of dynamic payment amount threshold:

[0086] Th x =Th base ⋅α⋅(1+0.1⋅log (1+f day / f T ))

[0087] α= N success / N total +0.2⋅tanh(1 / UT avg )

[0088] Among them, Th x is the dynamic payment amount threshold, α is the channel reliability coefficient, f day is the average daily trading frequency, f T is the total historical transaction frequency, UT avg is the channel reliability coefficient, N success is the number of successful transactions on the channel, N total The channel reliability coefficient can be determined as the channel transaction number and the average channel response time.

[0089] Optionally, after the step of determining the dynamic payment amount threshold corresponding to the available payment channel, the method further includes: when the current time period is reached, updating the channel reliability coefficient according to the average daily transaction frequency to obtain the channel reliability coefficient corresponding to the current time period; when a fluctuation event is detected in the available payment channel within the current time period, compensating the dynamic payment amount threshold by using the channel reliability coefficient corresponding to the current time period to obtain the compensated dynamic payment amount threshold.

[0090] In the embodiment of the present invention, the calibration reliability coefficient is performed every ΔT=6 hours, as shown in the following formula:

[0091] α′=α⋅[1+( N success − f day ) / ( N success +f day +ϵ) ]

[0092] Where ϵ is the smoothing factor, and the value range of ϵ is [0.1, 0.3].

[0093] When the next time period is reached, the compensation item is injected to obtain the dynamic payment amount threshold Th after compensation. x ′, specifically as follows:

[0094] Th x ′=Th x +α′⋅Q p

[0095] After the dynamic payment amount threshold is reached after compensation, it is used to determine whether the order to be paid is a recent high-value multi-category order.

[0096] Optionally, the step of performing association calculation of commodity categories on the historical payment commodity category set to obtain the dynamic category quantity threshold corresponding to the available payment channel specifically includes: determining the transaction preference level of each commodity category based on the transaction success rate of each commodity category in the historical payment commodity category set; determining the commodity category association weight matrix based on the transaction preference level of the commodity category; and determining the dynamic category quantity threshold corresponding to the available payment channel based on the commodity category association weight matrix.

[0097] In this embodiment of the present invention, the LightGBM model can be used to extract the transaction preference level r∈[0,1] of the commodity category from the historical payment commodity category set, and calculate the commodity category association weight matrix:

[0098]

[0099] in, is the association weight between product category i and product category j, d is the similarity (measured distance) between product category i and product category j, is the transaction preference level of commodity category i, is the transaction preference level of commodity category j, Indicates normalization processing, converting the value to a value in [0,1].

[0100] The dynamic category number threshold is calculated as follows:

[0101]

[0102] in, is the dynamic category number threshold, is the standard deviation of the commodity category association weight matrix, is the trading frequency of the day, is the preset reference transaction frequency, Indicates taking the larger of the two values.

[0103] Optionally, the step of determining a recommended payment channel from among the multiple available payment channels based on the amount to be paid of the target user and the category of goods to be paid specifically includes: based on the amount to be paid and the category of goods to be paid, combined with the dynamic payment amount threshold corresponding to each of the available payment channels, and the dynamic category quantity threshold corresponding to each of the available payment channels, splitting the order to be paid to obtain multiple sub-orders to be paid; and determining a recommended payment channel from among the multiple available payment channels based on the multiple sub-orders to be paid.

[0104] In an embodiment of the present invention, for high-value, multi-category orders, if automatic payment splitting is allowed in the target user's account settings, a splitting plan can be dynamically generated based on order characteristics and payment channel restrictions, including splitting by amount ratio, splitting by product category grouping, and mixed splitting:

[0105] Among them, split by amount ratio: split the amount to be paid into several sub-orders, so that the amount of each sub-order is ≤ the dynamic amount threshold of at least one available payment channel.

[0106] Example: The total amount is 12,000 yuan, and the amount thresholds of payment channels A and B are 8,000 yuan and 6,000 yuan respectively. Then the order is split into a sub-order of 8,000 yuan (using A) and a sub-order of 4,000 yuan (using B).

[0107] Group and split by product category: Group products into multiple sub-orders by category, so that the number of categories in each sub-order is ≤ the dynamic category number threshold of the target payment channel.

[0108] Example: An order contains three categories of goods (electronics, food, and cross-border goods). The category thresholds for payment channels C and D are 2 and 1, respectively. The order is split into a sub-order for "electronics + food" (using C) and a sub-order for "cross-border goods" (using D).

[0109] Mixed splitting: Perform multi-dimensional splitting based on both amount and category restrictions.

[0110] Specifically, for mixed splitting, a structured analysis can be performed on the original order to extract the amount distribution of each category of goods and category correlation. The amount distribution can be, for example, the total amount and the independent pricing of sub-goods / services (such as 5,000 yuan for product A and 3,000 yuan for product B); category correlation can be, for example, the logical grouping of product categories (such as "electronic products" and "accessories" can be merged into the same category) and cross-category restriction rules (such as some payment channels prohibiting the simultaneous payment of "virtual goods" and "physical goods").

[0111] Based on the dynamic threshold of available payment channels, a split constraint model is constructed. The split constraint model includes amount constraints, category constraints, and business rules. The details are as follows:

[0112] Amount constraint: The amount of each sub-order must be less than or equal to the dynamic payment amount threshold of at least one available payment channel.

[0113] Category constraint: The number of categories included in each sub-order must be ≤ the dynamic category quantity threshold of at least one available payment channel.

[0114] Business rules: such as non-separable goods (for example, gifts must be paid for together with the main goods), split account requirements (for example, different merchants must settle independently).

[0115] In a possible embodiment, after the splitting constraint model is constructed, priority-driven splitting can be adopted under the constraints of the splitting constraint model, such as amount priority or category priority.

[0116] Amount priority: If exceeding the limit is the primary concern, the order is split by amount threshold first, and then the sub-orders are grouped by category. For example, if the total amount is 15,000 yuan (exceeding the limit), it is split into 10,000 yuan (using channel M) + 5,000 yuan (using channel N). The sub-order categories are then checked to see if they meet the target channel restrictions.

[0117] Category priority: If a category exceeds the threshold more severely, group the orders by category first, then adjust the sub-order amounts to within the channel threshold. Example: An order containing four categories of products is split into "Category 1+2" (using Channel P, Category Threshold 2) and "Category 3+4" (using Channel Q, Category Threshold 2). The amounts are then allocated so that each part is ≤ the channel amount threshold.

[0118] In another possible embodiment, after the split constraint model is constructed, an optimization objective function can be defined under the constraints of the split constraint model to balance the amount and category adaptability through weights, as follows:

[0119] Objective function = α*(amount fitness) + β*(category fitness)

[0120] Where α is the weight of the amount adaptation, β is the weight of the category adaptation, α+β=1, and is dynamically adjusted according to user or system policies.

[0121] Amount compatibility = sub-order amount / dynamic payment amount threshold of the target available payment channel (the closer to the threshold, the higher the compatibility);

[0122] Category suitability = number of sub-order categories / dynamic category number threshold of the target available payment channel (the closer to the threshold, the higher the suitability).

[0123] Greedy algorithm, genetic algorithm or integer programming can be used to solve the optimal split combination to obtain multiple sub-orders.

[0124] It should be noted that for non-separable items, products that need to be paid in full (such as bundled sales packages) can be marked to ensure that they are not split into different sub-orders; for cross-channel conflicts, if a product category is prohibited by multiple channels, it will be automatically assigned to an unrestricted channel; for user intervention, a split preview interface can be provided, allowing users to manually adjust the split ratio or specify the payment channel.

[0125] like Figure 2 As shown, an embodiment of the present invention provides an online payment device based on an aggregated payment system, and the online payment device based on the aggregated payment system includes:

[0126] The first processing module 201 is used to determine the amount to be paid and the category of goods to be paid for the order to be paid when the target user initiates payment;

[0127] The second processing module 202 is configured to, if it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels and the number of commodity categories to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, determine a recommended payment channel from the plurality of available payment channels based on the amount to be paid by the target user and the commodity categories to be paid, each of the available payment channels corresponding to a dynamic payment amount threshold;

[0128] The payment module 203 is configured to execute the payment operation of the target user based on the recommended payment channel when the target user determines that the final payment channel is the recommended payment channel.

[0129] Optionally, the device further includes:

[0130] An acquisition module is used to acquire the target user's historical payment information, wherein the historical payment information includes historical payment amounts, historical payment channels, and historical payment commodity categories;

[0131] The third processing module is configured to determine a dynamic payment amount threshold and a dynamic category quantity threshold corresponding to each of the available payment channels based on the historical payment amount, the historical payment channel, and the historical payment commodity category.

[0132] Optionally, the third processing module is also used to determine, for the historical payment channel, the historical payment amount set and the historical payment commodity category set corresponding to each of the available channels; perform quantile regression calculation on the historical payment amount set to obtain the dynamic payment amount threshold corresponding to the available payment channel; perform commodity category association calculation on the historical payment commodity category set to obtain the dynamic category quantity threshold corresponding to the available payment channel.

[0133] Optionally, the third processing module is also used to determine the payment amount corresponding to the target percentile based on the historical payment amount in the historical payment amount set; determine the transaction activity weight, the historical total transaction frequency and the average daily transaction frequency based on the historical payment time in the historical payment amount set; obtain the channel reliability coefficient of the available payment channel and the number of successful transactions of the channel; determine the dynamic payment amount threshold corresponding to the available payment channel based on the payment amount corresponding to the target percentile, the transaction activity weight, the historical total transaction frequency, the average daily transaction frequency, the channel reliability coefficient and the number of successful transactions of the channel.

[0134] Optionally, the third processing module is also used to update the channel reliability coefficient according to the average daily transaction frequency when the current time period is reached, so as to obtain the channel reliability coefficient corresponding to the current time period; when a fluctuation event is detected in the available payment channel within the current time period, the dynamic payment amount threshold is compensated and calculated using the channel reliability coefficient corresponding to the current time period to obtain the compensated dynamic payment amount threshold.

[0135] Optionally, the third processing module is also used to determine the transaction preference level of each commodity category based on the transaction success rate of each commodity category in the historical payment commodity category set; determine the commodity category association weight matrix based on the transaction preference level of the commodity category; and determine the dynamic category quantity threshold corresponding to the available payment channel based on the commodity category association weight matrix.

[0136] Optionally, the second processing module 202 is also used to split the order to be paid based on the amount to be paid and the category of goods to be paid, combined with the dynamic payment amount threshold corresponding to each of the available payment channels, and the dynamic category quantity threshold corresponding to each of the available payment channels, to obtain multiple sub-orders to be paid; based on the multiple sub-orders to be paid, determine the recommended payment channel from the multiple available payment channels.

[0137] It should be noted that the online payment device based on the aggregated payment system provided in the embodiment of the present invention can be applied to computers, servers and other devices that can perform online payment methods based on the aggregated payment system.

[0138] The online payment device based on the aggregated payment system provided in the embodiment of the present invention can implement each process implemented by the online payment method based on the aggregated payment system in the above method embodiment and can achieve the same beneficial effects. To avoid repetition, it will not be described here.

[0139] See also Figure 3 , Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, it includes: a memory 302, a processor 301, and a computer program of an online payment method based on an aggregated payment system stored in the memory 302 and executable on the processor 301, wherein:

[0140] The processor 301 is configured to call the computer program stored in the memory 302 and execute the following steps:

[0141] When the target user initiates payment, the amount to be paid and the category of goods to be paid for the order to be paid are determined;

[0142] If it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels, and the number of commodity categories to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, then based on the amount to be paid by the target user and the commodity categories to be paid, a recommended payment channel is determined from the multiple available payment channels, each of the available payment channels corresponding to a dynamic payment amount threshold;

[0143] When the target user determines that the final payment channel is the recommended payment channel, the payment operation of the target user is performed based on the recommended payment channel.

[0144] Optionally, before the step of determining that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels, and the number of commodity categories to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, the method executed by the processor 301 further includes:

[0145] Obtaining the target user's historical payment information, wherein the historical payment information includes historical payment amounts, historical payment channels, and historical payment commodity categories;

[0146] Based on the historical payment amount, the historical payment channel and the historical payment commodity category, a dynamic payment amount threshold and a dynamic category quantity threshold corresponding to each of the available payment channels are determined.

[0147] Optionally, the step of determining, by the processor 301, the dynamic payment amount threshold and the dynamic category quantity threshold corresponding to each of the available payment channels based on the historical payment amount, the historical payment channel, and the historical payment commodity category specifically includes:

[0148] For the historical payment channels, determining the historical payment amount set and the historical payment commodity category set corresponding to each available channel;

[0149] Performing quantile regression calculation on the historical payment amount set to obtain a dynamic payment amount threshold corresponding to the available payment channel;

[0150] An association calculation of commodity categories is performed on the historical payment commodity category set to obtain a dynamic category quantity threshold corresponding to the available payment channel.

[0151] Optionally, the step of performing quantile regression calculation on the historical payment amount set to obtain the dynamic payment amount threshold corresponding to the available payment channel, performed by the processor 301, includes:

[0152] Determining a payment amount corresponding to a target percentile based on historical payment amounts in the historical payment amount set;

[0153] Determine the transaction activity weight, the total historical transaction frequency, and the average daily transaction frequency based on the historical payment time of the historical payment amount concentration;

[0154] Obtaining a channel reliability coefficient and a number of successful transactions of the available payment channel;

[0155] Based on the payment amount corresponding to the target percentile, the transaction activity weight, the total historical transaction frequency, the average daily transaction frequency, the channel reliability coefficient and the number of successful transactions of the channel, a dynamic payment amount threshold corresponding to the available payment channel is determined.

[0156] Optionally, after the step of determining the dynamic payment amount threshold corresponding to the available payment channel, the method executed by the processor 301 further includes:

[0157] When the current time period is reached, the channel reliability coefficient is updated according to the average daily transaction frequency to obtain the channel reliability coefficient corresponding to the current time period;

[0158] When a fluctuation event is detected in the available payment channel during the current time period, the dynamic payment amount threshold is compensated and calculated using the channel reliability coefficient corresponding to the current time period to obtain a compensated dynamic payment amount threshold.

[0159] Optionally, the step of performing association calculation of commodity categories on the historical payment commodity category set to obtain a dynamic category quantity threshold corresponding to the available payment channel, performed by the processor 301, specifically includes:

[0160] determining a transaction preference level for each commodity category based on a transaction success rate for each commodity category in the historical payment commodity category set;

[0161] Determining a commodity category association weight matrix based on the transaction preference levels of the commodity categories;

[0162] Based on the commodity category association weight matrix, a dynamic category quantity threshold corresponding to the available payment channel is determined.

[0163] Optionally, the step of determining a recommended payment channel from the multiple available payment channels based on the amount to be paid by the target user and the category of the goods to be paid, performed by the processor 301, specifically includes:

[0164] Based on the amount to be paid and the category of the goods to be paid, combined with the dynamic payment amount threshold corresponding to each of the available payment channels and the dynamic category quantity threshold corresponding to each of the available payment channels, the order to be paid is split to obtain multiple sub-orders to be paid;

[0165] Based on the multiple sub-orders to be paid, a recommended payment channel is determined from the multiple available payment channels.

[0166] It should be noted that the electronic device provided in the embodiment of the present invention can be applied to computers, servers and other devices that can perform online payment methods based on the aggregated payment system.

[0167] The electronic device provided in the embodiment of the present invention can implement each process of the online payment method based on the aggregate payment system in the above method embodiment and can achieve the same beneficial effects. To avoid repetition, it will not be described here.

[0168] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the computer program implements the various processes of the online payment method based on the aggregated payment system provided by the embodiment of the present invention and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0169] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The computer-readable storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0170] The above disclosure is merely a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. An online payment method based on an aggregated payment system, wherein the aggregated payment system has multiple available payment channels, wherein the available payment channels are payment channels that have been bound and verified by the user, characterized in that: The method comprises the following steps: When the target user initiates payment, the amount to be paid and the category of goods to be paid for the order to be paid are determined; Acquire the target user's historical payment information, including historical payment amounts, historical payment channels, and historical payment commodity categories; For the historical payment channels, determining a historical payment amount set and a historical payment commodity category set corresponding to each available payment channel; Perform quantile regression calculation on the historical payment amount set to obtain the dynamic payment amount threshold corresponding to the available payment channel; specifically determine the payment amount corresponding to the target percentile based on the historical payment amount in the historical payment amount set; determine the transaction activity weight, the historical total transaction frequency and the average daily transaction frequency based on the historical payment time in the historical payment amount set; obtain the channel reliability coefficient of the available payment channel and the number of successful transactions of the channel; determine the dynamic payment amount threshold corresponding to the available payment channel based on the payment amount corresponding to the target percentile, the transaction activity weight, the historical total transaction frequency, the average daily transaction frequency, the channel reliability coefficient and the number of successful transactions of the channel; wherein, extract the historical payment amount data set , perform sliding window quantile calculation and obtain the basic payment threshold: in, As the basic payment threshold, is the minimum quantile, is the maximum quantile, t represents the tth historical payment information in the historical payment amount dataset, Indicates the The historical payment amount corresponding to the historical payment information, w is the time window, Indicates rounding up, H is the empirical coefficient, and the value range of H is [0.10, 0.40]; Calculation of dynamic payment amount threshold: in, is the dynamic payment amount threshold, α is the channel reliability coefficient, is the average daily trading frequency, is the total historical transaction frequency, is the number of successful transactions on the channel, is the number of channel transactions; Perform commodity category association calculation on the historical payment commodity category set to obtain the dynamic category quantity threshold corresponding to the available payment channel; specifically, determine the transaction preference level of each commodity category based on the transaction success rate of each commodity category in the historical payment commodity category set; determine the commodity category association weight matrix based on the transaction preference level of the commodity category; determine the dynamic category quantity threshold corresponding to the available payment channel based on the commodity category association weight matrix; extract the transaction preference level r∈[0,1] of the commodity category in the historical payment commodity category set through the LightGBM model, and calculate the commodity category association weight matrix: in, is the association weight between product category i and product category j, d is the similarity between product category i and product category j, is the transaction preference level of commodity category i, is the transaction preference level of commodity category j, Indicates normalization processing, converting the value into a value in [0,1]; The dynamic category number threshold is calculated as follows: in, is the threshold value of the number of dynamic categories, is the standard deviation of the commodity category association weight matrix, is the trading frequency of the day, is the preset reference transaction frequency, Indicates taking the larger of the two values; If it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels, and the number of commodity categories to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, then based on the amount to be paid by the target user and the commodity categories to be paid, a recommended payment channel is determined from the multiple available payment channels, each of the available payment channels corresponding to a dynamic payment amount threshold; When the target user determines that the final payment channel is the recommended payment channel, the payment operation of the target user is performed based on the recommended payment channel.

2. The online payment method based on the aggregated payment system according to claim 1, characterized in that: After the step of determining the dynamic payment amount threshold corresponding to the available payment channel, the method further includes: When the current time period is reached, the channel reliability coefficient is updated according to the average daily transaction frequency to obtain the channel reliability coefficient corresponding to the current time period; When a fluctuation event is detected in the available payment channel during the current time period, the dynamic payment amount threshold is compensated and calculated using the channel reliability coefficient corresponding to the current time period to obtain a compensated dynamic payment amount threshold.

3. The online payment method based on the aggregated payment system according to claim 1 or 2, characterized in that: The step of determining a recommended payment channel from the plurality of available payment channels based on the amount to be paid by the target user and the category of the goods to be paid specifically includes: Based on the amount to be paid and the category of the goods to be paid, combined with the dynamic payment amount threshold corresponding to each of the available payment channels and the dynamic category quantity threshold corresponding to each of the available payment channels, the order to be paid is split to obtain multiple sub-orders to be paid; Based on the multiple sub-orders to be paid, a recommended payment channel is determined from the multiple available payment channels.

4. An online payment device based on an aggregated payment system, wherein the aggregated payment system has multiple available payment channels, wherein the available payment channels are payment channels that have been bound and verified by the user, characterized in that: The online payment device based on the aggregated payment system includes: The first processing module is used to determine the amount to be paid and the category of goods to be paid for the order to be paid when the target user initiates payment; An acquisition module is used to acquire the target user's historical payment information, wherein the historical payment information includes historical payment amounts, historical payment channels, and historical payment commodity categories; A third processing module is configured to determine, for the historical payment channels, a historical payment amount set and a historical payment commodity category set corresponding to each of the available payment channels; Perform quantile regression calculation on the historical payment amount set to obtain the dynamic payment amount threshold corresponding to the available payment channel; specifically determine the payment amount corresponding to the target percentile based on the historical payment amount in the historical payment amount set; determine the transaction activity weight, the historical total transaction frequency and the average daily transaction frequency based on the historical payment time in the historical payment amount set; obtain the channel reliability coefficient of the available payment channel and the number of successful transactions of the channel; determine the dynamic payment amount threshold corresponding to the available payment channel based on the payment amount corresponding to the target percentile, the transaction activity weight, the historical total transaction frequency, the average daily transaction frequency, the channel reliability coefficient and the number of successful transactions of the channel; wherein, extract the historical payment amount data set , perform sliding window quantile calculation and obtain the basic payment threshold: in, As the basic payment threshold, is the minimum quantile, is the maximum quantile, t represents the tth historical payment information in the historical payment amount dataset, Indicates the The historical payment amount corresponding to the historical payment information, w is the time window, Indicates rounding up, H is the empirical coefficient, and the value range of H is [0.10, 0.40]; Calculation of dynamic payment amount threshold: in, is the dynamic payment amount threshold, α is the channel reliability coefficient, is the average daily trading frequency, is the total historical transaction frequency, is the number of successful transactions on the channel, is the number of channel transactions; Perform commodity category association calculation on the historical payment commodity category set to obtain the dynamic category quantity threshold corresponding to the available payment channel; specifically, determine the transaction preference level of each commodity category based on the transaction success rate of each commodity category in the historical payment commodity category set; determine the commodity category association weight matrix based on the transaction preference level of the commodity category; determine the dynamic category quantity threshold corresponding to the available payment channel based on the commodity category association weight matrix; extract the transaction preference level r∈[0,1] of the commodity category in the historical payment commodity category set through the LightGBM model, and calculate the commodity category association weight matrix: in, is the association weight between product category i and product category j, d is the similarity between product category i and product category j, is the transaction preference level of commodity category i, is the transaction preference level of commodity category j, Indicates normalization processing, converting the value into a value in [0,1]; The dynamic category number threshold is calculated as follows: in, is the threshold value of the number of dynamic categories, is the standard deviation of the commodity category association weight matrix, is the trading frequency of the day, is the preset reference transaction frequency, Indicates taking the larger of the two values; a second processing module configured to, if it is determined that the amount to be paid is greater than the dynamic payment amount threshold corresponding to each of the available payment channels and the number of commodity categories to be paid is greater than the dynamic category number threshold corresponding to each of the available payment channels, determine a recommended payment channel from the plurality of available payment channels based on the amount to be paid by the target user and the commodity categories to be paid, each of the available payment channels corresponding to a dynamic payment amount threshold; The payment module is configured to execute a payment operation of the target user based on the recommended payment channel when the target user determines that the final payment channel is the recommended payment channel.

5. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the online payment method based on the aggregated payment system as claimed in any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the online payment method based on the aggregated payment system according to any one of claims 1 to 3.

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