Data processing method and system based on dynamic routing

Abstract

The invention relates to a data processing method and system based on dynamic routing. The method comprises the following steps: receiving a transaction request initiated by a user; the transaction request comprises merchant information, a payment mode and a transaction amount of the user; obtaining a currently available payment channel; performing multi-dimensional scoring on each payment channel to obtain a scoring result of each payment channel; determining an optimal payment channel according to the scoring result of each payment channel; processing the transaction request by using the selected optimal payment channel; and if the transaction fails, re-scoring other payment channels, and selecting a suboptimal payment channel according to a scoring result until the transaction succeeds or all payment channels are tried. The payment channel selection accuracy, the payment efficiency and the user experience can be improved.

Description

Technical Field

[0001] The present invention relates to the field of electronic transaction technology, and in particular to a data processing method, system, electronic device and computer-readable storage medium based on dynamic routing. Background Art

[0002] In existing electronic transaction systems, payment channel selection is usually based on pre-set static rules or fixed path allocation algorithms. These systems usually use fixed routing strategies to allocate transaction requests to various channels according to the channel's historical performance or the operator's priority. This static allocation mechanism can meet basic needs in some environments with low transaction volume or single transaction type.

[0003] However, static routing cannot be dynamically adjusted according to the real-time changing transaction channel conditions, which can easily cause channel overload or resource waste. Especially during peak trading periods, some channels may experience delays or failures due to overload operation, affecting user experience. Secondly, the existing system is difficult to comprehensively select the optimal channel based on multi-dimensional indicators such as transaction risk, processing efficiency and cost, resulting in some transactions not being processed in a timely and efficient manner. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a data processing method, system, electronic device and non-transitory computer-readable storage medium based on dynamic routing, which can improve the accuracy of payment channel selection, payment efficiency and user experience.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a data processing method based on dynamic routing, the method comprising:

[0007] Receiving a transaction request initiated by a user; the transaction request includes the user's merchant information, payment method and transaction amount;

[0008] Get currently available payment channels;

[0009] Perform multi-dimensional scoring on each of the payment channels to obtain a scoring result for each of the payment channels;

[0010] Determine the optimal payment channel according to the scoring results of each of the payment channels;

[0011] Processing the transaction request using the selected optimal payment channel;

[0012] If the transaction fails, the other payment channels are re-scored, and the next best payment channel is selected based on the scoring results until the transaction succeeds or all payment channels are tried.

[0013] The present invention also provides a data processing system based on dynamic routing, the system comprising:

[0014] A request receiving module, used to receive a transaction request initiated by a user; the transaction request includes the user's merchant information, payment method and transaction amount;

[0015] The channel acquisition module is used to obtain currently available payment channels;

[0016] A channel scoring module is used to perform multi-dimensional scoring on each of the payment channels to obtain a scoring result for each of the payment channels;

[0017] A channel selection module, used to determine the optimal payment channel according to the scoring results of each payment channel;

[0018] A transaction processing module, used for processing the transaction request using the selected optimal payment channel;

[0019] The channel reselection module is used to re-score the other payment channels if the transaction fails, and select the next best payment channel according to the scoring results until the transaction is successful or all payment channels are tried.

[0020] In addition, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing a data processing method based on dynamic routing as described above.

[0021] In addition, to achieve the above objectives, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, it implements a data processing method based on dynamic routing as described above.

[0022] The beneficial effects of the present invention are:

[0023] The present invention can acquire and analyze multi-dimensional data of each transaction channel in real time, such as payment success rate, settlement cycle, fee rate, abnormal order processing speed, transaction volume, error rate and payment amount limit, etc., comprehensively evaluate the performance of each channel, and ensure that transaction requests are always allocated to the optimal channel. Compared with the traditional static routing method, this solution can dynamically adjust channel selection according to real-time data, improving the flexibility and efficiency of the transaction system.

[0024] In summary, the present invention can achieve smarter and more efficient payment channel selection, which not only optimizes the overall performance of the transaction system, but also reduces operating costs and improves system stability and user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a data processing method based on dynamic routing provided by the present invention;

[0026] Figure 2 A structural schematic diagram of a data processing system based on dynamic routing provided by the present invention;

[0027] Figure 3 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0028] Figure 4 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0031] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0032] See also Figure 1 , provides a flow chart of a data processing method based on dynamic routing of the present invention, comprising the following steps:

[0033] Step 201: Receive a transaction request initiated by a user.

[0034] The transaction request may include the user's merchant information, payment method and transaction amount.

[0035] In some embodiments, the merchant information may include the merchant identity or number to which the user belongs, which is used to identify the specific source of the transaction, such as the merchant ID, merchant type, and the above-mentioned agreement.

[0036] Payment method is the means by which users choose to complete transactions. Payment methods may include bank card payment, e-wallet, bank transfer, cash on delivery, digital currency payment, etc.

[0037] The transaction amount is the specific amount of the user's transaction, which directly affects the complexity of transaction processing and risk assessment. The transaction amount can include the total amount and currency type.

[0038] In the present invention, the merchant information, payment method and transaction amount included in the user's transaction request will serve as the key basis for the system to select a payment channel. The system can calculate the comprehensive score of each channel through a scoring formula based on this information, and dynamically select the payment channel that best suits the current transaction to ensure the success rate, security and economy of the transaction.

[0039] Step 202: Obtain currently available payment channels.

[0040] It is understandable that before determining the most suitable payment channel for this transaction request, it is necessary to obtain all currently available payment channels in order to evaluate and screen them from among the payment channels.

[0041] In some embodiments, before step 203, the present invention may further include:

[0042] Obtain the average processing time of each of the payment channels for historical transactions;

[0043] Obtain the error rate of each payment channel for historical transactions, etc.;

[0044] According to the payment success rate, average processing time and error rate of each payment channel, historical transaction data of each payment channel is obtained.

[0045] In some embodiments, the operating status of each payment channel can be checked in real time to exclude channels that are temporarily unavailable due to technical failures, system maintenance, etc. The risk control status of each channel for the current merchant can be checked to exclude channels that have blacklisted the merchant. Based on the amount of the current transaction, channels that support the amount range can be screened out. It can be ensured that the selected channel supports the payment method selected by the user (such as credit card, debit card, e-wallet, etc.).

[0046] Among them, the average processing time reflects the efficiency of the channel and has a direct impact on the user experience. The error rate reflects the stability and reliability of the channel. The payment success rate is a key indicator to measure the performance of the channel.

[0047] In some embodiments, real-time status and historical data can be combined to obtain a more comprehensive channel performance portrait. For example, a payment channel may have a high historical success rate, but is currently in an unstable state. Another payment channel may have a long average processing time, but a low error rate.

[0048] It is understandable that this acquisition process should be dynamic, and the system should continuously monitor and update this data so that decisions can be made based on the latest information at each transaction. You can consider using efficient data storage and analysis systems (such as time series databases and real-time analysis tools) to process this data to ensure that this information can be quickly acquired and processed even in high-concurrency scenarios.

[0049] The present invention can adjust the weight of these data in a personalized way according to the characteristics and needs of different merchants. For example, for merchants who focus on stability, they may pay more attention to channels with low error rates. Through such a comprehensive and dynamic data acquisition and analysis process, the system can select the most suitable payment channel for each transaction, thereby improving the overall transaction success rate and user satisfaction.

[0050] In some embodiments, the present invention may further include:

[0051] Obtain the success rate, real-time performance, rate and settlement period of the transaction by the user;

[0052] Obtaining the user's processing preference for abnormal orders;

[0053] The user's transaction preference is determined based on the user's preferences for transaction success rate, real-time performance, fee rate and settlement cycle, as well as the processing preference for abnormal orders.

[0054] In some embodiments, the transaction preferences of users (or merchants corresponding to users) may include success rate preferences: for example, some users may place more emphasis on the success rate of transactions, and may accept slightly higher fees. Real-time preferences: for example, some users may require transactions to be completed immediately and are particularly sensitive to processing time. Rate preferences: for example, for some users, low rates may be the primary consideration. Settlement cycle preferences: for example, some users may place more emphasis on fast settlement to improve cash flow.

[0055] In some embodiments, users can set preferences directly, their historical transaction behaviors can be analyzed, information can be collected through questionnaires or interviews, and their preferences can be obtained by inferring based on the industry to which the users belong or the size of their companies.

[0056] In some embodiments, different users may have different processing preferences, which may include, for example, processing speed: some users may want abnormal orders to be resolved quickly. Processing method: some users may prefer automated processing, while other users may prefer manual intervention. Notification method: users may have different preferences, such as phone, email, SMS, etc. Refund policy: when an order is abnormal, users may have different refund processing preferences.

[0057] In some embodiments, a comprehensive transaction preference profile can be created for each user based on the information collected above. For example, the transaction preference profile can include preference weight allocation: weights are allocated to different factors (success rate, real-time performance, rates, etc.). Preference model construction: machine learning algorithms (such as decision trees, random forests, etc.) can be used to build user preference models. Preference classification: users can be classified, such as those who focus on success rate, those who focus on low rates, etc. Dynamic adjustment: As user behavior changes, the system should be able to dynamically adjust these preference settings.

[0058] It can be understood that the process of determining the user's transaction preference in the present invention is crucial for the subsequent payment channel selection. It allows the system to consider not only the performance of the channel itself, but also the specific needs and preferences of the user when evaluating and selecting the payment channel. For example, for a user who focuses on success rate, the system may give priority to channels with a high historical success rate, even if its rate is slightly higher. For a user who is sensitive to cash flow, the system may give priority to channels with a short settlement cycle. For a user who often has large transactions, the system may give priority to channels that support high-value transactions and have high exception handling efficiency.

[0059] By determining the user's personalized preferences in the above manner, the present invention can provide each user with a payment experience that best suits his or her needs, thereby improving user satisfaction and the overall performance of the platform.

[0060] Step 203: Perform multi-dimensional scoring on each of the payment channels to obtain a scoring result for each of the payment channels.

[0061] Among them, the scoring dimensions of the scoring results of each payment channel include at least: payment success rate, settlement cycle, rate, abnormal transaction order processing speed, availability, merchant risk control status, and payment amount limit of each payment channel.

[0062] In some embodiments, the present invention may further include:

[0063] Continuously monitor the availability of each of the payment channels and the merchant risk control status;

[0064] Continuously record whether the payment amount of the user is greater than the payment amount limit;

[0065] When any one of the dimensions of the availability of the payment channel, the merchant's risk control status, and the user's payment amount is abnormal, the transaction request is terminated.

[0066] In some embodiments, the operating status of each payment channel can be continuously checked, such as periodically sending heartbeat requests, monitoring response time, checking error rates, etc. If a channel suddenly becomes unavailable (such as system failure, network problems, etc.), the system can immediately detect it.

[0067] In some embodiments, the risk control status of each merchant in each payment channel can be tracked in real time. For example, real-time communication is maintained with the risk control system of each payment channel. If a merchant is blacklisted by a channel or the risk level is increased, the system can be informed in time.

[0068] In some embodiments, the transaction amount limit information for each payment channel can be maintained and updated in a timely manner. These limits may change due to factors such as time, user type, transaction type, etc. For each transaction, the system needs to check in real time whether its amount exceeds the limit of the selected channel. This check should be performed before the transaction begins and should also be performed continuously during the transaction to cope with possible changes in the amount.

[0069] It is understandable that when a channel is unavailable (such as system failure, maintenance, etc.), a merchant is included in the risk control blacklist or the risk level suddenly increases, or the transaction amount exceeds the channel limit, once any of the three abnormal situations is detected, action should be taken immediately, such as terminating the current transaction request and trying to switch to other appropriate channels.

[0070] When a transaction is terminated, the system can promptly notify relevant parties (such as users, merchants, customer support teams, etc.). The notification should include the reason for the termination and possible next steps. The system has corresponding exception handling mechanisms, such as automatic retry, manual intervention, etc. For terminated transactions, the system should record detailed information for subsequent analysis and improvement.

[0071] By promptly detecting and responding to problems, reducing the possibility of transaction failures, preventing potential fraudulent transactions or over-limit transactions, and avoiding users wasting time on unavailable or inappropriate channels, we ensure the stability and reliability of the entire payment system and ensure that all transactions are conducted within the rules and limits set by all parties.

[0072] The comprehensive and strict real-time monitoring and control mechanism of the present invention can provide users with a smooth and reliable payment experience while ensuring security and compliance.

[0073] Step 204: Determine the optimal payment channel according to the scoring results of each payment channel.

[0074] In some embodiments, step 204 may include:

[0075] Obtaining the scoring dimensions and historical transaction data of the payment channel;

[0076] Obtaining characteristic values ​​of abnormal order processing speed, expected minimum daily transaction volume, acceptable maximum error rate, and expected minimum payment amount upper limit of the payment channel;

[0077] Obtaining a first adjustment coefficient, a second adjustment coefficient, a third adjustment coefficient, a fourth adjustment coefficient and a fifth adjustment coefficient, which are respectively used to adjust the payment success rate, the fee rate, the transaction volume, the error rate and the upper limit of the payment amount of the payment channel;

[0078] The scoring result of the payment channel is determined based on the scoring dimensions of the payment channel, historical transaction data, characteristic values ​​of abnormal order processing speed, expected minimum daily transaction volume, acceptable maximum error rate, expected minimum payment amount upper limit, first adjustment coefficient, second adjustment coefficient, third adjustment coefficient, fourth adjustment coefficient and fifth adjustment coefficient.

[0079] In some embodiments, the scoring result of the payment channel is expressed as:

[0080]

[0081] Among them, Score is the scoring result, S is the payment success rate, T is the settlement cycle, R is the fee rate, P is the abnormal transaction order processing speed, V is the transaction volume of the payment channel, E is the error rate of the payment channel, M is the upper limit of the payment amount of the payment channel, ω1, ω2, ω3, ω4 are the first weight, the second weight, the third weight and the fourth weight respectively, a1 and a2 are the first adjustment coefficient and the second adjustment coefficient respectively, T0 is the ideal settlement cycle threshold, k is the first control coefficient, P0 is the characteristic value of the abnormal order processing speed, V0 is the expected minimum daily transaction volume, E0 is the maximum acceptable error rate, M0 is the expected minimum payment amount upper limit, b1 and b2 are the first influence coefficient and the second influence coefficient respectively, c1 is the third influence coefficient, and m is the first control coefficient.

[0082] In the specific implementation, S is the payment success rate, which is a decimal between 0 and 1. T is the settlement cycle, in days. R is the fee rate, which is a decimal between 0 and 1. P is the processing speed of abnormal transaction orders, which is a score of 1-10. V is the transaction volume of the payment channel, which is a score of 1-10. E is the error rate of the payment channel, which is a decimal between 0 and 1. M is the maximum transaction amount of the payment channel, ω1, ω2, ω3, ω4 are the first weight, the second weight, the third weight and the fourth weight respectively, a1 and a2 are the first adjustment coefficient and the second adjustment coefficient respectively, T0 is the ideal settlement cycle threshold, k is the first control coefficient, P0 is the characteristic value of the abnormal order processing speed, V0 is the expected minimum daily transaction volume, E0 is the acceptable maximum error rate, M0 is the expected minimum and maximum transaction amount, b1 and b2 are the first influence coefficient and the second influence coefficient respectively, c1 is the third influence coefficient, and m is the first control coefficient.

[0083] In the specific implementation, They represent the impact of payment success rate, settlement cycle, fee rate, and abnormal order processing speed. Each indicator has a corresponding weight coefficient and adjustment coefficient to reflect its relative importance in the comprehensive score. S is the payment success rate, which ranges from 0 to 1 and represents the success probability of the payment channel completing the transaction. ω1 is the first weight, which is used to control the influence of the payment success rate on the score. a1 is the first adjustment coefficient, which is used to adjust the nonlinear effect of the payment success rate. If it is greater than 1, the channel with a higher success rate will get a higher score gain; if a1 is less than 1, the channel with a low success rate will be punished more.

[0084] In the formula, T is the settlement cycle, which indicates the time from the initiation of the transaction to the settlement of the funds, usually in days. ω2 is the second weight, which controls the influence of the settlement cycle on the score. T0 is the ideal settlement cycle threshold, which indicates the settlement cycle desired by the system. k is the first control coefficient, which adjusts the steepness of the curve of the settlement cycle's influence on the score. This part uses the Sigmoid function (S-shaped curve). The closer T is to T0, the higher the score.

[0085] In the formula, R is the fee rate, which represents the cost of using the payment channel, ranging from 0 to 1. ω3 is the third weight, which controls the influence of the fee rate on the score. a2 is the second adjustment coefficient, which is used to adjust the nonlinear impact of the fee rate. If it is less than 1, the channel with a low fee rate will get a higher score, and if it is greater than 1, the channel with a high fee rate will be punished more.

[0086] In , P is the abnormal order processing speed, which indicates the channel's ability to handle abnormal orders, and its value range is 1 to 10. ω4 is the fourth weight, which controls the influence of abnormal order processing speed on the score. P0 is the characteristic value of abnormal order processing speed, which is used to standardize the influence of this indicator. Using the exponential decay function, the score of this part increases with the increase of P. The faster the processing speed, the higher the score.

[0087] In the formula, V is the daily transaction volume of the payment channel. V0 is the expected minimum daily transaction volume. When the actual transaction volume V of the channel exceeds V0, the score of this part will increase. b1 is the first impact coefficient, which controls the impact of transaction volume on the score. This item improves the score by rewarding the transaction volume exceeding V0. When V>V0, the channel with larger transaction volume will get extra points; when V≤V0, this score remains at 1.

[0088] In the formula, E is the error rate of the payment channel, which indicates the proportion of failed transactions or errors. E0 is the maximum acceptable error rate threshold. When the error rate E exceeds E0, the score will be reduced. b2 is the second impact coefficient, which controls the penalty of the error rate on the score. When E>E0, the channel with a higher error rate will be punished more severely; when E≤E0, the score will not be affected.

[0089] In the formula, M is the upper limit of the payment amount of the payment channel, indicating the maximum single transaction amount supported by the channel. M0 is the expected minimum upper limit of the payment amount. When the upper limit M supported by the channel exceeds M0, the score increases. c1 is the third influence coefficient, which controls the impact of the upper limit of the payment amount on the score. m controls the steepness of the upper limit of the payment amount score curve, and tanh is the hyperbolic tangent function, which makes the score grow smoothly. Channels with larger upper limits of payment amounts will receive additional points.

[0090] The present invention comprehensively considers multiple key factors such as payment success rate, settlement cycle, rate, abnormal order processing speed, transaction volume, error rate, payment amount limit, etc., and the influence of each factor is controlled by weight and nonlinear adjustment coefficient. The flexibility of calculating the scoring result is very high, which is suitable for different business needs and scenarios. By adjusting the parameters, the contribution of each factor to the scoring result can be accurately controlled.

[0091] In some embodiments, step 204 may include:

[0092] Get the multi-objective optimization algorithm based on NSGA-II;

[0093] The optimal payment channel is determined according to the multi-objective optimization algorithm combined with the scoring results of each payment channel.

[0094] NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a multi-objective optimization algorithm that is particularly suitable for dealing with multiple potentially conflicting objectives. In the payment channel selection scenario, we do face multiple objectives that need to be optimized simultaneously, such as success rate, fee rate, processing speed, etc.

[0095] It can be understood that the present invention can find the best payment channel selection in a complex multi-dimensional decision space by using an advanced multi-objective optimization algorithm such as NSGA-II. It not only improves the quality of decision-making, but also can flexibly balance multiple potentially conflicting objectives, thereby providing the best payment experience for different users and transaction scenarios.

[0096] In some embodiments, in addition to the above method of determining the optimal payment channel, the present invention can also calculate the spatial distance of each channel through Euclidean distance, and screen the optimal payment channel through Euclidean distance.

[0097] Euclidean distance is the straight-line distance between two points in a multidimensional space. In the scenario of the present invention, it can be used to measure the distance between each payment channel and the ideal payment channel. Since different dimensions may have different units and ranges, the data needs to be standardized first. Commonly used methods include Min-Max scaling and Z-scale standardization. In the standardized multidimensional space, an ideal point is defined, such as success rate = 1 (highest), rate = 0 (lowest), processing time = 0 (fastest), etc. In some embodiments, for each actual payment channel, its Euclidean distance from the ideal point is calculated, and then sorted from small to large according to the Euclidean distance. The channel with the smallest distance is considered to be the best.

[0098] In some embodiments, the present invention may further include:

[0099] real-time transaction data according to the transaction request;

[0100] Obtaining market changes of the transaction scenario corresponding to the transaction request;

[0101] According to the transaction data and the market changes, the parameter values ​​of the parameters used to calculate the scoring results are automatically adjusted.

[0102] In some embodiments, real-time transaction data may include transaction amount, payment method, transaction time, transaction type, and merchant information. These data can be used to analyze the needs and characteristics of current transactions in real time, thereby helping the system to make a preliminary judgment on which channels should be selected and whether the transaction has certain special processing requirements (such as high security, low latency, etc.). The system starts the scoring formula with these real-time data and uses this information as input variables to calculate the initial score of each payment channel.

[0103] In some embodiments, market changes may include channel load conditions, peak transaction hours, payment policy changes, financial or currency market fluctuations, and security risk changes.

[0104] In some embodiments, the weights can be adjusted dynamically. For example, if there is an increased security risk in the market, the system may increase the weight of security-related indicators (such as payment success rate S and abnormal order processing speed P) and reduce the priority of other aspects.

[0105] In some embodiments, each adjustment coefficient can be adjusted. For example, in a high-risk market environment, the system may increase the nonlinear adjustment coefficient a1 of the payment success rate to give channels with higher success rates greater bonus points. If the market becomes more sensitive to transaction fees, the system may reduce the nonlinear adjustment coefficient a2 of the rate to give channels with lower rates greater advantages.

[0106] By receiving real-time data of transaction requests and combining market changes in transaction scenarios, the present invention can dynamically adjust various parameters in the scoring formula according to the current environment. This process ensures that the scoring formula can flexibly adapt as market conditions change, so that the most appropriate payment channel can be selected at each transaction moment. The adjusted scoring is more in line with actual needs, ensuring the efficiency, cost control and security of transactions.

[0107] Step 205: Use the selected optimal payment channel to process the transaction request.

[0108] It can be understood that after a series of operations such as score calculation, market change analysis, parameter adjustment, etc., the optimal payment channel is finally selected and the transaction request initiated by the user is executed through this channel. This process is a key step in the payment process, ensuring that the transaction is successfully carried out on the selected channel and can achieve the expected goals of the system, such as completing the payment quickly, safely and at low cost.

[0109] Step 206: If the transaction fails, the other payment channels are re-scored, and a suboptimal payment channel is selected based on the scoring results until the transaction succeeds or all payment channels are tried.

[0110] In some embodiments, the reasons for transaction failure may include insufficient funds: insufficient balance in the user's account to complete the payment. Unavailable payment method: the payment method selected by the user is invalid or restricted. Channel overload: the payment channel cannot process transactions normally during peak hours or network congestion. Network problem: there may be problems with the communication between the system and the payment channel, resulting in the transaction request not being successfully sent or processed. Abnormal situation: if abnormal behavior or security risks are detected during the transaction, the transaction is aborted.

[0111] Based on the results of the re-scoring, the channel with the second highest score can be selected from the remaining payment channels. This channel is usually an option with a score close to the optimal channel, but was not prioritized in the initial selection for some reason (such as slightly higher fees, slightly longer settlement cycle, etc.). After the transaction fails, the suboptimal channel will be used by the system as the new preferred channel to process the transaction.

[0112] Through this dynamic multiple scoring and channel switching mechanism, this solution can maximize the transaction success rate and minimize the payment failure caused by improper channel selection. This not only improves the user experience, but also helps merchants ensure that more transactions can be completed smoothly and reduce potential losses.

[0113] The present invention can enhance the flexibility of the system through the above mechanism, so that even if the initial channel selection fails, the success rate of the transaction can still be improved by automatically switching to other channels.

[0114] See also Figure 2 , Figure 2 A structural schematic diagram of a data processing system based on dynamic routing provided by the present invention.

[0115] like Figure 2 As shown, a data processing system based on dynamic routing proposed in an embodiment of the present invention includes:

[0116] The request receiving module 301 is used to receive a transaction request initiated by a user; the transaction request includes the user's merchant information, payment method and transaction amount;

[0117] A channel acquisition module 302 is used to acquire currently available payment channels;

[0118] A channel scoring module 303 is used to perform multi-dimensional scoring on each of the payment channels to obtain a scoring result for each of the payment channels;

[0119] A channel selection module 304 is used to determine the optimal payment channel according to the scoring result of each payment channel;

[0120] A transaction processing module 305, configured to process the transaction request using the selected optimal payment channel;

[0121] The channel reselection module 306 is used to re-score the other payment channels if the transaction fails, and select a suboptimal payment channel according to the scoring results until the transaction is successful or all payment channels are tried.

[0122] See also Figure 3 , Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0123] Receiving a transaction request initiated by a user; the transaction request includes the user's merchant information, payment method and transaction amount;

[0124] Get currently available payment channels;

[0125] Perform multi-dimensional scoring on each of the payment channels to obtain a scoring result for each of the payment channels;

[0126] Determine the optimal payment channel according to the scoring results of each of the payment channels;

[0127] Processing the transaction request using the selected optimal payment channel;

[0128] If the transaction fails, the other payment channels are re-scored, and the next best payment channel is selected based on the scoring results until the transaction succeeds or all payment channels are tried.

[0129] See also Figure 4 , Figure 4 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0130] Receiving a transaction request initiated by a user; the transaction request includes the user's merchant information, payment method and transaction amount;

[0131] Get currently available payment channels;

[0132] Perform multi-dimensional scoring on each of the payment channels to obtain a scoring result for each of the payment channels;

[0133] Determine the optimal payment channel according to the scoring results of each of the payment channels;

[0134] Processing the transaction request using the selected optimal payment channel;

[0135] If the transaction fails, the other payment channels are re-scored, and the next best payment channel is selected based on the scoring results until the transaction succeeds or all payment channels are tried.

[0136] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0137] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0139] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0141] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0142] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A data processing method based on dynamic routing, characterized in that: The method comprises: Receiving a transaction request initiated by a user; the transaction request includes the user's merchant information, payment method and transaction amount; Get currently available payment channels; Perform multi-dimensional scoring on each of the payment channels to obtain a scoring result for each of the payment channels; Determine the optimal payment channel according to the scoring results of each of the payment channels; Processing the transaction request using the selected optimal payment channel; If the transaction fails, the other payment channels are re-scored, and the next best payment channel is selected based on the scoring results until the transaction succeeds or all payment channels are tried.

2. The data processing method based on dynamic routing according to claim 1, characterized in that: The scoring dimensions of the scoring results of each payment channel include at least: payment success rate, settlement cycle, fee rate, abnormal transaction order processing speed, availability, merchant risk control status, and payment amount limit of each payment channel.

3. The data processing method based on dynamic routing according to claim 2, characterized in that: The method further comprises: Continuously monitor the availability of each of the payment channels and the merchant risk control status; Continuously record whether the payment amount of the user is greater than the payment amount limit; When any one of the dimensions of the availability of the payment channel, the merchant's risk control status, and the user's payment amount is abnormal, the transaction request is terminated.

4. The data processing method based on dynamic routing according to claim 3 is characterized in that: Before performing multi-dimensional scoring on each payment channel, the method further includes: Obtain the average processing time of each of the payment channels for historical transactions; Obtain the error rate of each payment channel for historical transactions, etc.; According to the payment success rate, average processing time and error rate of each payment channel, historical transaction data of each payment channel is obtained.

5. The data processing method based on dynamic routing according to claim 4, characterized in that: The method further comprises: Obtain the success rate, real-time performance, rate and settlement period of the transaction by the user; Obtaining the user's processing preference for abnormal orders; The user's transaction preference is determined based on the user's preferences for transaction success rate, real-time performance, fee rate and settlement cycle, as well as the processing preference for abnormal orders.

6. The data processing method based on dynamic routing according to claim 5, characterized in that: The multi-dimensional scoring of each payment channel to obtain the scoring result of each payment channel includes: Obtaining the scoring dimensions and historical transaction data of the payment channel; Obtaining characteristic values ​​of abnormal order processing speed, expected minimum daily transaction volume, acceptable maximum error rate, and expected minimum payment amount upper limit of the payment channel; Obtaining a first adjustment coefficient, a second adjustment coefficient, a third adjustment coefficient, a fourth adjustment coefficient and a fifth adjustment coefficient, which are respectively used to adjust the payment success rate, the fee rate, the transaction volume, the error rate and the upper limit of the payment amount of the payment channel; The scoring result of the payment channel is determined based on the scoring dimensions of the payment channel, historical transaction data, characteristic values ​​of abnormal order processing speed, expected minimum daily transaction volume, acceptable maximum error rate, expected minimum payment amount upper limit, first adjustment coefficient, second adjustment coefficient, third adjustment coefficient, fourth adjustment coefficient and fifth adjustment coefficient.

7. The data processing method based on dynamic routing according to claim 6, characterized in that: The scoring result of the payment channel is expressed as: Among them, Score is the scoring result, S is the payment success rate, T is the settlement cycle, R is the fee rate, P is the abnormal transaction order processing speed, V is the transaction volume of the payment channel, E is the error rate of the payment channel, M is the upper limit of the payment amount of the payment channel, ω1, ω2, ω3, ω4 are the first weight, the second weight, the third weight and the fourth weight respectively, a1 and a2 are the first adjustment coefficient and the second adjustment coefficient respectively, T0 is the ideal settlement cycle threshold, k is the first control coefficient, P0 is the characteristic value of the abnormal order processing speed, V0 is the expected minimum daily transaction volume, E0 is the maximum acceptable error rate, M0 is the expected minimum payment amount upper limit, b1 and b2 are the first influence coefficient and the second influence coefficient respectively, c1 is the third influence coefficient, and m is the first control coefficient.

8. The data processing method based on dynamic routing according to claim 7, characterized in that: Determining the optimal payment channel according to the scoring result of each payment channel includes: Get the multi-objective optimization algorithm based on NSGA-II; The optimal payment channel is determined according to the multi-objective optimization algorithm combined with the scoring results of each payment channel.

9. The data processing method based on dynamic routing according to claim 8, characterized in that: The method further comprises: real-time transaction data according to the transaction request; Obtaining market changes of the transaction scenario corresponding to the transaction request; According to the transaction data and the market changes, the parameter values ​​of the parameters used to calculate the scoring results are automatically adjusted.

10. A data processing system based on dynamic routing, characterized in that: The system comprises: A request receiving module, used to receive a transaction request initiated by a user; the transaction request includes the user's merchant information, payment method and transaction amount; The channel acquisition module is used to obtain currently available payment channels; A channel scoring module is used to perform multi-dimensional scoring on each of the payment channels to obtain a scoring result for each of the payment channels; A channel selection module, used to determine the optimal payment channel according to the scoring results of each payment channel; A transaction processing module, used for processing the transaction request using the selected optimal payment channel; The channel reselection module is used to re-score the other payment channels if the transaction fails, and select the next best payment channel according to the scoring results until the transaction is successful or all payment channels are tried.

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