CN116823252A

CN116823252A - Funds path processing method and system

Info

Publication number: CN116823252A
Application number: CN202310673672.8A
Authority: CN
Inventors: 庄紫; 胡轩维
Original assignee: AlipayCom Co ltd
Current assignee: AlipayCom Co ltd
Priority date: 2023-06-07
Filing date: 2023-06-07
Publication date: 2023-09-29

Abstract

The specification provides a fund passageway processing method and a fund passageway processing system, which are used for obtaining an overrun probability based on the amount of a historical payment order of a user of a payment platform, so that the limit constraint of the user is converted into the overrun probability and is embedded into an objective function of a fund passageway distribution model, thereby removing user-level constraint conditions with long limit period and hundreds of millions of orders, wherein the constraint conditions of the fund passageway distribution model only comprise constraint conditions with smaller orders of orders, and the distribution efficiency of fund passageway distribution for the payment order is improved.

Description

Funds path processing method and system

Technical Field

The present disclosure relates to the field of online assignment, and in particular, to a method and system for processing a fund path.

Background

In recent years, the application scene of the online assignment problem is increasingly wide. In particular, the fund path on-line allocation scenario of the payment platform, the constraint condition of the fund path is often related to the constraint condition of the user level and the constraint condition of the fund path level, wherein the number of the constraint condition of the user level can reach hundreds of millions sometimes, and the constraint time of the two constraint conditions is usually different, so that the problem of on-line allocation of the fund path on-line allocation scenario of the payment platform is complicated, that is, the server is difficult to solve in a limited time, and thus, on-line allocation under the scenario is difficult to realize.

Therefore, it is necessary to provide a new method and system for processing a fund path, which can quickly and efficiently solve the problem of online assignment of the fund path of the payment platform in the online distribution scenario.

Disclosure of Invention

The fund path processing method and the fund path processing system provided by the specification can reduce the allocation time of the fund paths, thereby improving the efficiency of fund path allocation.

In a first aspect, the present description provides a funds pathway processing method comprising: a method for allocating a funds path to a payment order for a plurality of users of a paymate, comprising: determining a plurality of overrun probabilities corresponding to each first historical payment order in a plurality of first historical payment orders, wherein the plurality of overrun probabilities correspond to a plurality of preset fund paths, each overrun probability in the plurality of overrun probabilities comprises a probability that a user corresponding to the first historical payment order can overrun payment when the corresponding fund path is selected, and the probability represents a user-level payment quota constraint; determining an objective function of a fund path allocation model based on overrun probabilities corresponding to the plurality of first historical payment orders, so as to embed the user-level payment quota constraint carried by the overrun probabilities in the objective function, wherein the objective of the fund path allocation model comprises maximizing benefits of the paymate and minimizing overrun probabilities of each first historical payment order, constraint conditions comprise the plurality of fund path level quota constraints, and decision variables comprise fund paths corresponding to each first historical payment order; and determining an output of the fund path allocation model, the output of the fund path allocation model configured to allocate a corresponding fund path for an order to be paid for any user of the paymate.

In some embodiments, the probability of the excess payment comprises a probability that the predicted total amount of remaining payments exceeds a remaining user payment limit within a term of the user-level payment limit constraint.

In some embodiments, the determining the plurality of overrun probabilities for each of the plurality of first historical payment orders includes: determining, based on the probability density function of the user to which each first historical payment order belongs, the corresponding plurality of overrun probabilities of the user to which each first historical payment order belongs, wherein the probability density function of the user to which each first historical payment order belongs is obtained based on: for each of the users to which the plurality of first historical payment orders belong, determining a daily payment amount distribution rule of the current user and a probability density function corresponding to the daily payment amount distribution rule of the current user based on the plurality of second historical payment orders of the current user.

In some embodiments, the determining, based on the plurality of second historical payment orders of the current user, a daily payment amount distribution rule of the current user and a probability density function corresponding thereto includes: and obtaining a total daily payment amount of any user in the time length of the second historical payment orders based on the second historical payment orders of any user, and obtaining a daily payment amount distribution rule of the corresponding user and a probability density function corresponding to the daily payment amount distribution rule by adopting an empirical distribution histogram mode.

In some embodiments, said determining, for each of said first historical payment orders, said corresponding plurality of overrun probabilities based on said probability density function of the user to which it belongs comprises: determining that after a current first historical payment order, a user to which the current first historical payment order belongs is the remaining user payment limit within the term of the user-level payment limit constraint; and determining the overrun probabilities based on the probability density function of the user to which the current first historical payment order belongs and a plurality of preset quota occupation data, wherein the quota occupation data corresponds to the fund paths.

In some embodiments, the determining an objective function of the fund path allocation model based on overrun probabilities corresponding to the plurality of first historical payment orders includes: and determining the result of dividing the gains of a plurality of fund paths corresponding to each first historical payment order and the overrun probability as the parameters of the decision variable of the objective function.

In some embodiments, the determining the output of the funding pathway allocation model includes: and solving the fund path distribution model based on an original dual algorithm to obtain a result of dual variables of the fund path distribution model.

In some embodiments, the funds pathway processing method further comprises: distributing a target fund path for a target payment order from the plurality of fund paths based on the result of the dual variables and the overrun probability corresponding to the target payment order, wherein the target payment order is a payment order to be subjected to fund path distribution; and completing the target payment order through the target funds pathway.

In some embodiments, the allocating a target funds pathway for the target payment order from the plurality of funds pathways based on the outcome of the dual variables and the corresponding overrun probability for the target payment order comprises: constructing an objective function aiming at the objective payment order based on the result of the dual variables and the overrun probability corresponding to the objective payment order, wherein the objective of the objective function comprises maximizing the income of the objective payment order and minimizing the overrun probability corresponding to the objective payment order; and determining the target funding pathway based on the objective function.

In a second aspect, the present specification also provides a funds pathway processing system comprising: a server, the server comprising: at least one storage medium storing at least one instruction set for performing allocation of a funds path; and at least one processor in communication with the at least one storage medium, wherein the at least one processor reads the at least one instruction set and performs the funds pathway processing method of the first aspect described above in accordance with the at least one instruction set when the funds pathway processing system is operating.

According to the technical scheme, the fund path processing method and the fund path processing system provided by the specification can obtain the overrun probability based on the amount of the historical payment order of the user of the payment platform, so that the quota constraint of the user is converted into the overrun probability and is embedded into the objective function of the fund path distribution model, and therefore the user-level constraint condition with a long limiting period and hundreds of millions of orders of magnitude can be removed, and the constraint condition of the fund path distribution model only comprises constraint conditions with smaller orders of magnitude, so that the distribution efficiency of fund path distribution for the payment order is improved.

Additional functions of the funds path processing methods and systems provided herein will be set forth in part in the description which follows. The following numbers and examples presented will be apparent to those of ordinary skill in the art in view of the description. The inventive aspects of the present specification of funds path processing methods and systems may be fully explained by the practice or use of the methods, devices, and combinations described in the following detailed examples.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present description, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 illustrates an application scenario diagram of a funds pathway processing system provided in accordance with embodiments of the present description;

FIG. 2 illustrates a hardware architecture diagram of a computing device provided in accordance with an embodiment of the present description;

FIG. 3 illustrates a schematic construction of a fund path allocation model provided in accordance with an embodiment of the present disclosure;

FIG. 4 illustrates a flow chart of a method of processing a funds pathway provided in accordance with embodiments of the present description; and

fig. 5 shows a flow chart of another fund path processing method provided in accordance with an embodiment of the present description.

Detailed Description

The following description is presented to enable one of ordinary skill in the art to make and use the invention, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the disclosure. Thus, the present description is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. For example, as used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. The terms "comprises," "comprising," "includes," and/or "including," when used in this specification, are taken to specify the presence of stated integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

These and other features of the present specification, as well as the operation and function of the related elements of structure, as well as the combination of parts and economies of manufacture, may be significantly improved upon in view of the following description. All of which form a part of this specification, reference is made to the accompanying drawings. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the description. It should also be understood that the drawings are not drawn to scale.

The flowcharts used in this specification illustrate operations implemented by systems according to some embodiments in this specification. It should be clearly understood that the operations of the flow diagrams may be implemented out of order. Rather, operations may be performed in reverse order or concurrently. Further, one or more other operations may be added to the flowchart. One or more operations may be removed from the flowchart.

For convenience of description, this specification first explains terms that may appear later:

on-line assignment problem: the optimal decision is selected from a plurality of possible choices in real time. Usually, such problems need to determine corresponding decision variables, objective functions and constraint conditions in an actual use scene, and then obtain the result of the decision variables for realizing the objective functions under the condition that the constraint conditions are met, namely the optimal decision. The online assignment problem in which both the objective function and the constraint are linear is called a linear assignment problem.

Decision variables: refers to an amount or object that may be altered or adjusted. These variables are controllable or operable. For example, in the manufacturing industry, the production quantity may be a decision variable. In the financial field, the investment or consumption amount may be a decision variable. In the transportation field, the number of vehicles may be a decision variable.

Objective function: typically a mathematical expression, is used to characterize the goal that is desired to be maximized or minimized. For example, in the manufacturing industry, the objective function may be to maximize yield or minimize cost. In the transportation field, the objective function may be to minimize transportation costs or maximize traffic. In the financial field, the objective function may be to maximize profit or minimize risk.

Constraint conditions: rules and constraints that limit decision variables. These conditions must be met to obtain an optimal solution. Constraints may be physical, technical, environmental, or other. For example, in the manufacturing industry, the constraint may be equipment capacity or the number of workers. In the financial field, the constraint may be a risk limit or a funds limit for the portfolio. In the transportation field, the constraint may be the transportation time or the loading capacity. We can define constraints using equations, inequalities, equations, etc.

Soft constraints and hard constraints: under certain tolerance conditions, soft constraints are constraints that can be broken through. Accordingly, a hard constraint is a rigid constraint that cannot be broken through.

Click through rate: CTR (Click Through Rate), i.e., the ratio of the number of times a web advertisement is clicked to the number of accesses. For example, for a web page, where the web page includes several advertisements, if the web page is accessed 100 times and the advertisements on the page are clicked 20 times, the CTR is 20%, and it is visible that the CTR is one of indexes for evaluating the advertisement putting effect.

And (3) a payment platform: the payment platform is short for a third party electronic payment platform, and refers to a platform which is used for realizing money payment, cash circulation, fund clearing and query statistics among consumers, financial institutions and merchants by establishing connection between merchants and banks through communication, computer and information security technologies. The front end of the third party payment platform directly faces the online clients, and the rear end of the third party payment platform is connected with each commercial bank or connected with each commercial bank through a people bank payment system. The payment platform can be a fund 'middle platform' of the buyer and the seller in the transaction process, and is an independent mechanism for guaranteeing the interests of the buyer and the seller under the supervision of a bank. In the transaction through the payment platform, after purchasing goods, the buyer (user) can use various payment modes provided by the payment platform to pay for the goods of the order, for example, the user can choose to pay by adopting the account balance of the payment platform, for example, the user can pay by adopting a bank card of a certain bank bound with the account number of the payment platform, the payment platform informs the seller of the arrival of the goods for shipment after the payment is completed, and after collecting and checking the goods by the buyer, the payment platform confirms the payment to the seller, and the payment platform transfers the money to the account of the seller.

Independent co-distribution (IID, independent Identically Distribution): in probability statistics, IID refers to that in the random process, values at any moment are random variables, and if the random variables follow the same distribution and are independent of each other, the random variables satisfy the independent same distribution. For example, if the random variables X1 and X2 are independent, it means that the value of X1 does not affect the value of X2, the value of X2 does not affect the value of X1, and the random variables X1 and X2 follow the same distribution, which means that X1 and X2 have the same distribution shape and the same distribution parameters, have the same distribution law for the discrete random variables, have the same probability density function for the continuous random variables, have the same distribution function, and have the same expectations and variances.

The online assignment problem can be applied to various scenes, such as a fund path online distribution scene, an advertisement flow online regulation scene, a logistics matching scene, an online marketing recommendation scene and other intelligent online distribution scenes. In some online assignment problems, the number of constraints is large, which can make solving the online assignment problem difficult. For example, for a scenario in which a paymate allocates a fund path for a user's payment order, the corresponding constraint may generally include both a user-level payment limit constraint (corresponding to the number of users) for a long constraint period and a fund path-level limit constraint (corresponding to the number of fund paths) for a short constraint period, since the total number of users using the paymate may often reach the order of billions, the number of user-level constraint may reach the order of billions, so that the total number of constraint and the total number of decision variables are both the order of billions, which increases the complexity of solving the online assignment problem, increases the difficulty of solving, and decreases the computational efficiency, which results in extremely low efficiency of solving the fund path problem allocated for the user's payment order, and generally makes it difficult to satisfy the real-time requirement of online assignment.

Fig. 1 shows a schematic diagram of an application scenario of a funds path processing system 001 according to an embodiment of the present disclosure. The fund path processing system 001 (hereinafter referred to as system 001) may be applied to any online assignment scenario (a scenario suitable for obtaining an optimal solution by adopting a solution manner of an online assignment problem), for example, an online fund path allocation scenario, an online advertisement flow regulation scenario, a logistics matching scenario, an online marketing recommendation scenario, and other intelligent online allocation scenarios. As shown in fig. 1, the system 001 may include the target terminal 200 and the server 300, and accordingly, an application scenario of the system 001 may include: target user 100, system 001, and communication network 400.

Wherein the target user 100 may be an operator of the target terminal 200. The target terminal 200 may interact with the target user 100, and the target user 100 may trigger an online allocation task of a fund path of the payment order through the target terminal 200, for example, when the target user 100 performs online payment (such as online purchase, code scanning payment, etc.) through a client application installed on the target terminal 200 by a payment platform, the server 300 needs to allocate an appropriate fund path for the payment order in real time after receiving the payment request, so as to complete online payment of the target user 100.

In some embodiments, the target terminal 200 may be an intelligent electronic device. For example, the target terminal 200 may include a mobile device, a tablet computer, a notebook computer, a built-in device of a motor vehicle, or the like, or any combination thereof. For example, the mobile device may include a smart home device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. For example, the smart home device may include a smart television, a desktop computer, etc., or any combination thereof. For example, the smart mobile device may include a smart phone, personal digital assistant, gaming device, navigation device, etc., or any combination thereof.

In some embodiments, at least one Application (APP) may be installed on the target terminal 200. The APP can provide the target user 100 with the ability to interact with the outside world via network 400 as well as an interface. Wherein each of the applications may comprise computer program code. The computer program code may include, but is not limited to, programs, routines, objects, components, data structures, procedures, modules, and the like. The at least one application includes a target application. As an example, the target application may include, but is not limited to, a client application corresponding to a payment platform that is in the billion level and that can make online payments, such as a digital living open platform, a shopping open platform, a game open platform, and the like.

The server 300 may be a background server of the paymate, and the server 300 may implement various functions of the target application and allocate appropriate funds pathways for the payment operations to complete the payment operations. That is, the target application is a client application corresponding to the server 300 to provide a local service to the target user 100. The target application may communicate with the server 300 through the target terminal 200, so that the server 300 may provide services to the target user 100 through the target terminal 200. In some embodiments, the server 300 may be communicatively coupled to a plurality of terminals (not shown in FIG. 1). In some embodiments, the target terminal 200 may interact with the server 300 via a communication network 400 to send a message to the server 300 triggering the server 300 to complete the online allocation of funds paths for an order payment.

The communication network 400 is a medium for providing a communication connection between the target terminal 200 and the server 300. The communication network 400 may facilitate the exchange of information or data. As shown in fig. 1, the target terminal 200 and the server 300 may be connected to a communication network 400 and mutually transmit information or data through the communication network 400. In some embodiments, the communication network 400 may be any type of wired or wireless network, or a combination thereof. For example, communication network 400 may include a cable network, a fiber-optic network, a telecommunications network, an intranet, the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), bluetooth ^TM Network, zigBee ^TM A network, a Near Field Communication (NFC) network, or the like. In some embodiments, communication network 400 may include one or more network access points. For example, the communication network 400 may include a wired or wireless network access point, such as a base station or an internet switching point, through which one or more components of the target terminal 200 and the server 300 may be connected to the communication network 400 to exchange data or information.

It should be understood that the number of target terminals 200, servers 300 and peer communication networks 400 in fig. 1 is merely illustrative. Any number of target terminals 200, servers 300, and communication networks 400 may be present in the scene 001, as desired for implementation.

Fig. 2 illustrates a hardware architecture diagram of a computing device 600 provided in accordance with an embodiment of the present description. The computing device 600 may perform the funds path processing methods described herein, which will be described in detail elsewhere in this specification.

As shown in fig. 2, computing device 600 may include at least one storage medium 630 and at least one processor 620. In some embodiments, computing device 600 may also include a communication port 650 and an internal communication bus 610. Meanwhile, computing device 600 may also include I/O component 660.

Internal communication bus 610 may connect the various system components including storage medium 630, processor 620, and communication ports 650.

I/O component 660 supports input/output between computing device 600 and other components.

The communication port 650 is used for data communication between the computing device 600 and the outside world, for example, the communication port 650 may be used for data communication between the computing device 600 and the communication network 400. The communication port 650 may be a wired communication port or a wireless communication port.

The storage medium 630 may include a data storage device. The data storage device may be a non-transitory storage medium or a transitory storage medium. For example, the data storage devices may include one or more of magnetic disk 632, read Only Memory (ROM) 634, or Random Access Memory (RAM) 636. The storage medium 630 further includes at least one set of instructions stored in the data storage device. The instructions are computer program code that may include programs, routines, objects, components, data structures, procedures, modules, etc. that perform the funds path processing methods provided herein.

The at least one processor 620 may be communicatively coupled with at least one storage medium 630 and a communication port 650 via an internal communication bus 610. The at least one processor 620 is configured to execute the at least one instruction set. When the computing device 600 is running, the at least one processor 620 reads the at least one instruction set and, as directed by the at least one instruction set, performs the fund path processing methods provided herein. The processor 620 may perform all of the steps involved in the funds path processing method. The processor 620 may be in the form of one or more processors, and in some embodiments, the processor 620 may include one or more hardware processors, such as microcontrollers, microprocessors, reduced Instruction Set Computers (RISC), application Specific Integrated Circuits (ASICs), application specific instruction set processors (ASIPs), central Processing Units (CPUs), graphics Processing Units (GPUs), physical Processing Units (PPUs), microcontroller units, digital Signal Processors (DSPs), field Programmable Gate Arrays (FPGAs), advanced RISC Machines (ARM), programmable Logic Devices (PLDs), any circuit or processor capable of executing one or more functions, or the like, or any combination thereof. For illustrative purposes only, only one processor 620 is depicted in the computing device 600 in this specification. It should be noted, however, that computing device 600 may also include multiple processors, and thus, operations and/or method steps disclosed in this specification may be performed by one processor as described herein, or may be performed jointly by multiple processors. For example, if the processor 620 of the computing device 600 performs steps a and B in this specification, it should be understood that steps a and B may also be performed by two different processors 620 in combination or separately (e.g., a first processor performs step a, a second processor performs step B, or the first and second processors perform steps a and B together).

The online assignment problem can be applied to various scenes, such as a fund path online distribution scene, an advertisement flow online regulation scene, a logistics matching scene, an online marketing recommendation scene and other intelligent online distribution scenes. In some online assignment problems, the number of constraints is large, which can make solving the online assignment problem difficult. For example, for the scenario that the paymate allocates funds paths for the user's payment order, since the corresponding paymate allocates funds paths for the user's payment order, the corresponding constraint may generally include both a long constraint period user-level payment allowance constraint (N, equivalent to the number of users) and a short constraint period funds path-level allowance constraint (K, equivalent to the number of funds paths), since the total number of users using the paymate may often reach hundreds of millions, the number of user-level constraint N may reach hundreds of millions, and thus the total number of constraint n+k and the total number of decision variables N are hundreds of millions, which increases the complexity of solving the online assignment problem, increases the difficulty of solving, and decreases the calculation efficiency, which results in extremely low efficiency of the paymate server to solve the funds paths for the user's payment order, and generally makes it difficult to meet the real-time requirement of online assignment.

In order to solve the problem that constraint conditions and decision variables are hundreds of millions in order of magnitude and constraint periods of the constraint conditions are inconsistent under the condition that the payment platform distributes a fund path for a payment order of a user, the specification provides a fund path processing method. It should be understood by those skilled in the art that the fund path processing method and system described in the present specification may also be applied to an application scenario where the number of other constraints and the number of decision variables are billions and/or the constraints thereof include on-line allocation (real-time planning) problems of both short-period constraints and long-period constraints, and are within the scope of protection of the present specification.

In general, the number of users N of the paymate may be: the total number of users registering corresponding accounts at the paymate or the total number of all users paying using the paymate within a certain time. The paymate may store each payment order for which a plurality of users of the paymate have paid through the paymate. The payment order may include information such as payment time, payment amount, whether payment was successful or not, and reasons for failure of payment of the payment order. The payment platform can allocate a proper fund payment path for one payment order of any user using the payment platform in real time, so that the payment order of the corresponding user can be successfully paid, and the payment platform can maximize the income thereof.

Typically, the total amount of funds that each user uses the paymate to complete a payment within a first time window (e.g., one year) is limited. If each user has at least one sub-account under the account of the payment platform, the total amount of funds for each user to complete payment using any one of the at least one sub-account of the payment platform within the first time window may be limited, respectively. That is, the total amount of funds that each of the N users uses to complete payment using any one of the at least one sub-account within the first time window cannot exceed the corresponding preset user payment limit, otherwise the corresponding order will fail to pay. Also, typically, the capacity of each of the plurality of funding paths of the paymate is limited, i.e., for each of the K funding paths of the paymate, the total capacity used within a predetermined second time window (e.g., one day) cannot exceed the predetermined path capacity limit, otherwise the corresponding order will fail. The sub-accounts corresponding to different fund paths may be different, for example, if the user a has two sub-accounts, namely sub-account 1 and sub-account 2, then if the fund paths 1 to i occupy the corresponding preset user payment limits of sub-account 1, the fund paths i+1 to K occupy the corresponding preset user payment limits of sub-account 2, and for the order OrderA1 of the user a, if the fund paths 1 to i are selected, the corresponding preset user payment limits of sub-account 1 are occupied, and if the fund paths i+1 to K are selected, the corresponding preset user payment limits of sub-account 2 are occupied. Also, the cost per fund path is different for the paymate. For example: for user a, the corresponding payment order of OrderA2 is ten thousand yuan, the payment cost of using the fund path 1 is at least 0.1 yuan, the second time window is one day, the remaining capacity of the fund path 1 in one day is insufficient to complete the payment of the OrderA2, that is, the payment of the OrderA2 can cause the total capacity of the fund path 1 used in one day to exceed the preset path capacity limit, thereby causing the payment of the OrderA2 to fail, and in order to successfully complete the payment of the OrderA3, the payment platform allocates other fund paths, such as the fund path 3, for the OrderA3, the cost of the fund path 3 can be 0.15 yuan, which is slightly higher than the cost of the fund path 1 by 0.1 yuan, but the remaining capacity of the fund path 3 in one day can complete the payment of the OrderA2, and the payment platform can allocate the fund path 3 for the OrderA 3.

Next, a detailed description will be given of how the paymate allocates the funds paths for each payment order overall to achieve the allocation objective of maximizing the benefit. It should be noted that, the maximization of the benefit of the paymate may refer to maximization of the benefit of the paymate in one day, or may refer to maximization of the benefit of the paymate in one month or one year, because from a global perspective, if the paymate allocates funds for each payment order in an aggregate manner so as to maximize its profit, the paymate pays the greatest profit through the order at any time.

Because the problem of allocating funds paths to the payment orders of the users by the payment platform belongs to the linear programming problem, the solution can be carried out based on the solution mode of the linear programming problem. Based on this, computing device 600 may build a fund path allocation model corresponding to the fund path allocation problem for the payment order of the user. The initial objective of the funds path distribution model is to maximize the benefits of the paymate, and the initial objective function of the funds path distribution model may be the benefits of the paymate. The decision variables of the fund path allocation model are fund paths corresponding to a plurality of first historical payment orders, that is, the decision variables of the fund path allocation model can characterize the fund path selected from K sub-paths as the payment carrying the first historical payment order i for any one of the plurality of first historical payment orders i. The initial constraint conditions of the fund path allocation model comprise a plurality of user-level payment limit constraints and a plurality of fund path-level limit constraints, wherein the plurality of user-level payment limit constraints can comprise that the total payment amount of a user to which the first historical payment order i belongs in a first historical time window does not exceed a preset user payment limit; the plurality of funds path level limit constraints include that the total capacity of the paths used for each funds path within the second time window does not exceed the preset path capacity limit. The parameters of the objective function of the fund path allocation problem are parameters corresponding to decision variables of the fund path allocation problem, and may include benefits of each fund path. For example, assuming the first time window is one year, the second time window is one day, the certain time is one day, there are two sub-accounts under the paymate account, where the preset user payment limit for each sub-account is 20 ten thousand and 25 ten thousand, the preset path capacity limit is 10000 (the capacity consumption of the path is 1 every time the path is used). Constraints of the funds pathway allocation problem may include: each of the users to whom all historical payment orders belong in one day has a total payment amount of sub-account 1 not exceeding 20 ten thousand and a total payment amount of sub-account 2 not exceeding 25 ten thousand (the user-level payment limit constraint); the total capacity of the used funds paths for each funds path in a day does not exceed the preset path capacity limit 10000 (the funds path level limit constraint). The plurality of first historical payment orders may be all historical payment orders within the third time window, typically the third time window is one day, and the total number of users to which all historical payment orders within a day belong may often be on the order of billions, the order of the funds path limit constraint is on the order of the funds path, which is relatively small compared to the order of billions, and therefore the initial constraint count of the funds path allocation model is also on the order of billions, and the total number of decision variables of the funds path allocation model is also on the order of billions, which results in higher complexity in solving the funds path allocation model. Based on this, we consider the concept of using utility functions, marginal benefits and marginal risks to optimize the objective functions and constraints of the fund path distribution model, thereby reducing the solution complexity of the fund path distribution model. That is, if all payment orders can be successfully completed, and if so, the paymate selects the lowest cost fund path for each payment, the paymate yields the greatest. It may also be understood that the paymate benefit is maximized if the likelihood (probability) that the total amount paid by the user in the first time window exceeds the preset user payment limit is minimized (corresponding marginal benefit is higher and corresponding marginal risk is lower). Therefore, as shown in fig. 3, the user-level payment quota constraint carried by the overrun probability can be embedded in the initial objective function, and the user-level payment quota constraint is removed from the initial constraint condition, so that the objective function and the constraint condition of the fund path distribution model after optimization are obtained, the constraint condition is only the fund path level quota constraint, the number of the constraint conditions is greatly reduced, the solving complexity of the fund path distribution model can be further reduced, and the solving efficiency is improved.

Based on this, fig. 4 shows a flowchart of a funds path processing method P100 provided according to an embodiment of the present description. As previously described, computing device 600 may perform the fund path processing method P100 of the present specification. It should be noted that, in the method P100, the related data of the target user 100 and the data of other users are all authorized by the user.

As shown in fig. 4, the method P100 may include:

s110: determining a plurality of overrun probabilities corresponding to each first historical payment order in a plurality of first historical payment orders, wherein the plurality of overrun probabilities correspond to a plurality of preset fund paths, each overrun probability in the plurality of overrun probabilities comprises a probability that a user corresponding to the first historical payment order can overrun payment when the corresponding fund path is selected, and the probability represents a user-level payment quota constraint.

It should be noted that, the plurality of first historical payment orders are first historical payment orders accumulated by the paymate in a certain historical time. By way of example, the certain time may be one week, one day, one hour or five minutes, and other times of greater or lesser duration. If the certain historical time is one day, the plurality of first historical payment orders can be all historical payment orders in one day before the current time; if the certain historical time is one hour, the plurality of first historical payment orders may be all historical payment orders within one hour before the current time.

As described above, the decision variables of the fund path allocation model may characterize, for any one of the plurality of first historical payment orders i, which fund path is selected from the K sub-paths as the payment carrying the first historical payment order i, and the plurality of overrun probabilities corresponds to the preset K fund paths, so that for the first historical payment order i, there are K overrun probabilities corresponding thereto.

Next, a specific method of obtaining the overrun probability will be described in detail. In some embodiments, the probability of the excess payment comprises a probability that the predicted total amount of remaining payments exceeds a remaining user payment limit within a term of the user-level payment limit constraint. For ease of understanding we will describe the first time window as one year, the second time window as one day, the user level payment allowance as BU and the current time as t. The probability of the overrun payment may include: for user u to whom the current first historical payment order belongs, the predicted total amount of remaining payment is calculated within one yearProbability of exceeding the remaining user payment limit +.>

It can be seen that to obtainThe value of +.>Probability density function of (a). Since the total daily amount paid by the user u is unknown after the time t, the computing device 600 may obtain the total daily amount paid by the user of each first historical payment order in the third time window of the past by using statistical empirical distribution principle or by machine learning based on the second historical payment orders of the user of each first historical payment order in the third time window of the past, thereby obtaining->Probability density function of (a). For example, the total daily amount paid by the user to which each first historical payment order belongs may be within a third time window in the past, or the total daily amount paid by the user to which each first historical payment order belongs may be within a third time window in the past. Since each user satisfies IID between each payment order of the paymate, that is, each user is independent of each other, has no effect on each other, and satisfies the same probability distribution, it is obtained that the user to which each first historical payment order belongs also satisfies IID between the total amount paid by a plurality of days in the past third time window. At the same time, for a period of said plurality of said third time window of sufficient length, for example three years, for example five years, again The total amount paid per day satisfies a certain distribution law, i.e. obeys a certain probability distribution (or probability density function), e.g. a normal distribution. Thus, the computing device 600 may derive its empirical probability density function based on the total amount paid by the user to whom each of the first historical payment orders belongs over a plurality of days in a third time window of the past. In some embodiments, the computing device 600 may obtain the empirical probability distribution function of the total amount paid by the user to which each of the first historical payment orders belongs over a plurality of days in a third time window of the past in the manner of an empirical distribution histogram. It should be noted that the computing device 600 may obtain the +.>Or, obtaining said ++in real time>Probability density function of (a). That is, the computing device 600 may periodically calculate and update the probability density function of each of the plurality of users at a certain preset update period. For example, the computing device 600 calculates and updates +/once a day for each user of the paymate>Then, the funds pathway allocation model directly calls the probability density function of the user to which the corresponding payment order belongs each time it is used. For another example, the computing device 600 may also calculate, in real time, and use, the probability density function of the user to which the corresponding first historical payment order belongs at the current time, which is not limited in this embodiment of the present disclosure.

As previously described, for the same payment order, the occupation of the different sub-accounts of the user to which the payment order belongs may be different when different funding paths are selected. Therefore, the preset plurality of quota occupation data can represent occupation conditions of the same payment order on the quota of different sub-accounts of the user to which the payment order belongs when different fund paths are selected.

Specifically, the computing device 600 may obtain the plurality of overrun probabilities based on the probability density function of the user to which each of the first historical payment orders belongs and a remaining user payment allowance (a preset user payment allowance that may include different sub-accounts). For ease of understanding, assume that the first time window is T ₁ The second time window is T ₂ BU indicates a preset user payment limit for any one sub-account,at T for user u ₁ The total amount paid on day s, the predicted remaining total amount paid for the corresponding sub-account for the current time t after the current first historical order of payment is +.>The overrun probability of user u is +.>Wherein (1)>Representation->Is a function of the probability distribution of (c),can be based on->Obtained. The payment amount of the different payment orders of each user meets the independent same distribution, and the probability density accumulation convolution formula is +.>Using the fast fourier transform FFT and the inverse fast fourier transform IFFT theorem: />Correspondingly, a->Thereby get +.>Furthermore, can get +.>Probability values of (a) are provided. By adopting the method, the plurality of overrun probabilities corresponding to different fund paths corresponding to each payment order can be obtained only by obtaining the predicted remaining total payment amount of the corresponding sub-account under the condition of different fund paths. Since the FFT and IFFT have a computational complexity of O (nlogn), the method is ∈>The solving speed is very fast.

After obtaining the plurality of overrun probabilities, an initial objective function and initial constraints of the fund path distribution model may be optimized based on the overrun probabilities, resulting in an objective function and constraints of the (optimized) fund path distribution model. The specific steps are shown in S130.

S130: and determining an objective function of a fund path allocation model based on the overrun probabilities corresponding to the plurality of first historical payment orders, so as to embed the user-level payment quota constraint carried by the overrun probabilities in the objective function, wherein the objective of the fund path allocation model comprises maximizing the income of the paymate and minimizing the overrun probability of each first historical payment order, the constraint condition comprises the plurality of fund path level quota constraints, and the decision variable comprises the fund path corresponding to each first historical payment order.

As previously described, for the linear programming problem of the paymate allocating a funding path for each user's payment order, its corresponding initial allocation objective is to maximize the paymate's revenue. Based on the analysis, the user-level payment limit constraint is carried in the overrun probability, so that the overrun probability can be embedded into corresponding initial parameters, and the optimized parameters of the objective function can be obtained.

Based on this, in some embodiments, the funding pathway allocation model may be constructed based on: determining initial parameters of the objective function and initial constraint conditions of the fund path allocation model based on the allocation targets; obtaining parameters of decision variables corresponding to each user in the objective function based on the initial parameters of the objective function and the overrun probability; and obtaining the constraint condition based on the initial constraint condition. In some embodiments, the determining the constraint based on the initial constraint may include: removing the user-level payment quota constraint from the initial constraint condition, and determining the constraint condition as the fund pathway level quota constraint.

Specifically, in some embodiments, the determining the objective function of the funds path allocation model based on the overrun probabilities corresponding to the plurality of first historical payment orders includes: and determining the result of dividing the gains of a plurality of fund paths corresponding to each first historical payment order and the overrun probability as the parameters of the decision variable of the objective function. For easy understanding, let the first time window be T ₁ The second time window is T ₃ ，T ₁ >T ₂ The calculation time length of the maximum platform benefit is TP=T ₂ BU represents a preset user payment limit for any sub-account, BC represents a preset path capacity limit, and the decision variable isRepresented as any user u among the users to which the first historical payment order belongs is at T ₁ The ith payment order at the s moment in the inner is assigned with the jth passage, the value of which can be 0 or 1, wherein the value of 0 indicates that no corresponding j passage is allocated, and the value of 1 indicates that the corresponding j passage is allocated; the initial parameter is->Any user u in the users of the first historical payment order is at T ₁ The benefits of the paymate when the ith payment order at s time in the inner assigns the jth path; />Representing that any user u among the users to which the first historical payment order belongs is at T ₁ The ith payment order at s time in the time slot; />Representing that any user u among the users to which the first historical payment order belongs is at T ₁ The i-th payment order at time s within the time assigns the j-th path the capacity (resource) of the funding path occupied (consumed). The initial objective function of the fund path allocation model may be expressed as +.>The initial constraint can be expressed as +.>And +.>After optimization, the objective function of the fund path allocation model may be expressed asConstraint can be expressed as +.>Wherein (1)>

S150: determining an output of the fund path allocation model, the output of the fund path allocation model configured to allocate a corresponding fund path for an order to be paid for any user.

As described above, the fund path allocation model is constructed to solve the problem of allocating a fund path to a payment order of a user by a paymate, and thus, the output result of the fund path allocation model includes a solution to the problem of allocating a fund path to a payment order of a user by a paymate, that is, the computing device 600 may allocate a corresponding fund path to an order to be paid of any user based on the output result of the fund path allocation model. If a portion of the users completes the order to be paid, the remaining user payment limits of the sub-accounts corresponding to the corresponding funds paths of the portion of the users may change, so that when the computing device 600 subsequently determines the parameters of the objective function of the funds path allocation model again, the remaining user payment limits of the portion of the users need to be updated accordingly when the overrun probability is determined.

Specifically, in some embodiments, the determining the output result of the fund path allocation model may include: and solving the fund path distribution model based on an original dual algorithm to obtain a result of dual variables of the fund path distribution model. Accordingly, in some embodiments, as shown in fig. 5, P100 may further include S170.

S170: distributing a target fund path for a target payment order from the plurality of fund paths based on the result of the dual variables and the overrun probability corresponding to the target payment order, wherein the target payment order is a payment order to be subjected to fund path distribution; and completing the target payment order through the target funds pathway.

That is, the number of constraint conditions of the optimized funds path distribution model is the constraint number of funds path level, the number of decision variables is much larger than the constraint number of constraint conditions, so that the original dual algorithm can be adopted to solve the funds path distribution model to obtain corresponding constraint number of user levelDual variable lambda _j . Wherein lambda is _j The valuation of a unit's pathway resources under optimal utilization of the resources, also known as shadow prices, can be characterized in general, as well as the valuation of contributing to the resources in a practical application scenario. For example, assume that in the original dual solution method, the original problem is fund path resource allocation, BC is a quota of fund path resources, and each constraint condition of the fund path resources corresponds to a shadow price. The shadow price is different from the market price, which is known, and the shadow price is related to the utilization of the resource, i.e. the effect of the total economic benefit of utilizing the funding path resource. Thus, the computing device 600 is based on +. >Lambda of _j And allocating a corresponding fund path for the target payment order i ' which occurs at the time t ' of any user u '. In some embodiments, the allocating a target funds pathway for the target payment order from the plurality of funds pathways based on the outcome of the dual variables and the corresponding overrun probability for the target payment order comprises: constructing an objective function for the target payment order based on the result of the dual variables and the overrun probability corresponding to the target payment order, wherein the objective of the objective function comprises maximizationThe return of the target payment order and the overrun probability corresponding to the target payment order are minimized; and determining the target funding pathway based on the objective function. Specifically, the manner of determining the target fund path based on the objective function may be: />

That is, the computing device 600 selects the order for the target payment order i 'that user u' takes place at time tThe fund path corresponding to j with the maximum value is that of the corresponding +.>Others->It should be noted that +.>Is recalculated based on the actual order amount of said target payment order i' because the target payment order will cause a change in the remaining user payment limit of the user to which it belongs, i.e. +. >Overrun probability of payment order i 'occurring at time t' for user u

It should be noted that, a person skilled in the art may use an existing private dual module in matlab to solve the optimized funds path allocation model, or write a code to solve the optimized funds path allocation model, which is not limited in the embodiment of the present application.

According to the fund path processing method and the fund path processing system, the fund path can be distributed to the payment order of the user of the payment platform based on the output result of the fund path distribution model in a mode of solving the fund path distribution model.

The specification also provides a fund path processing method, which is used for processing the problems that constraint conditions and decision variables are hundreds of millions of orders and constraint periods of the constraint conditions are inconsistent under the condition that the payment platform distributes the fund path for a payment order of a user, by the following steps: and converting the constraint condition of the long constraint period into a soft constraint form, wherein the parameter is E, so that the period of the converted constraint condition is the same as the short constraint period. For example, assuming that the long constraint period is TL, the short constraint period is TS, BU pays a quota for a preset user in the constraint condition of the long constraint period, An ith payment order representing time s of user u within TL, the primary constraint condition of the long constraint period is: />Indicating that after time t in TL, the total payment amount of the payment order of user u cannot exceed +.>Wherein->The constraint conditions of the long constraint period after conversion are as follows:that is, even if user u is on a certain dayThe paid total amount breaks through the constraint upper limit of the current day scaling>If the prediction is performed according to the historical consumption condition and the payment liveness of the payment quota resource of the user u, so that the reasonable soft constraint punishment coefficient epsilon is selected, the influence on the result of the fund path distribution is not great. By the method, constraint periods of the user-level constraint and the fund path-level constraint can be kept consistent, and therefore the complexity of solving the fund path problem allocated to the payment order of the user by the payment platform can be reduced.

The specification provides a fund passage processing method and a fund passage processing system, which can obtain an overrun probability based on the amount of a historical payment order of a user of a payment platform, so that the quota constraint of the user is converted into the overrun probability and is embedded into an objective function of a fund passage distribution model, thereby removing user-level constraint conditions with long limiting period and hundreds of millions of orders, wherein the constraint conditions of the fund passage distribution model only comprise constraint conditions with smaller orders of orders, and the distribution efficiency of fund passage distribution for the payment order is improved.

In another aspect, the present description provides a non-transitory storage medium storing at least one set of executable instructions for performing an allocation. When executed by a processor, the executable instructions direct the processor to perform the steps of the funds pathway processing method P100 described herein. In some possible implementations, aspects of the specification can also be implemented in the form of a program product including program code. The program code is for causing the computing device 600 to perform the steps of the fund path processing method P100 described herein when the program product is run on the computing device 600. The program product for implementing the methods described above may employ a portable compact disc read only memory (CD-ROM) comprising program code and may run on computing device 600. However, the program product of the present specification is not limited thereto, and in the present specification, the readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system. The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Program code for carrying out operations of the present specification may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on computing device 600, partly on computing device 600, as a stand-alone software package, partly on computing device 600, partly on a remote computing device, or entirely on a remote computing device.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In view of the foregoing, it will be evident to a person skilled in the art that the foregoing detailed disclosure may be presented by way of example only and may not be limiting. Although not explicitly described herein, those skilled in the art will appreciate that the present description is intended to encompass various adaptations, improvements, and modifications of the embodiments. Such alterations, improvements, and modifications are intended to be proposed by this specification, and are intended to be within the spirit and scope of the exemplary embodiments of this specification.

Furthermore, certain terms in the present description have been used to describe embodiments of the present description. For example, "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present description. Thus, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined as suitable in one or more embodiments of the invention.

It should be appreciated that in the foregoing description of embodiments of the present specification, various features have been combined in a single embodiment, the accompanying drawings, or description thereof for the purpose of simplifying the specification in order to assist in understanding one feature. However, this is not to say that a combination of these features is necessary, and it is entirely possible for a person skilled in the art to label some of the devices as separate embodiments to understand them upon reading this description. That is, embodiments in this specification may also be understood as an integration of multiple secondary embodiments. While each secondary embodiment is satisfied by less than all of the features of a single foregoing disclosed embodiment.

Each patent, patent application, publication of patent application, and other materials, such as articles, books, specifications, publications, documents, articles, etc., cited herein are hereby incorporated by reference. All matters are to be interpreted in a generic and descriptive sense only and not for purposes of limitation, except for any prosecution file history associated therewith, any and all matters not inconsistent or conflicting with this document or any and all matters not complaint file histories which might have a limiting effect on the broadest scope of the claims. Now or later in association with this document. For example, if there is any inconsistency or conflict between the description, definition, and/or use of terms associated with any of the incorporated materials, the terms in the present document shall prevail.

Finally, it is to be understood that the embodiments of the application disclosed herein are illustrative of the principles of the embodiments of the present specification. Other modified embodiments are also within the scope of this specification. Accordingly, the embodiments disclosed herein are by way of example only and not limitation. Those skilled in the art can adopt alternative arrangements to implement the application in the specification based on the embodiments in the specification. Therefore, the embodiments of the present specification are not limited to the embodiments precisely described in the application.

Claims

1. A funds path processing method for allocating funds paths for payment orders of a plurality of users of a paymate, comprising:

determining a plurality of overrun probabilities corresponding to each first historical payment order in a plurality of first historical payment orders, wherein the plurality of overrun probabilities correspond to a plurality of preset fund paths, each overrun probability in the plurality of overrun probabilities comprises a probability that a user corresponding to the first historical payment order can overrun payment when the corresponding fund path is selected, and the probability represents a user-level payment quota constraint;

determining an objective function of a fund path allocation model based on overrun probabilities corresponding to the plurality of first historical payment orders, so as to embed the user-level payment quota constraint carried by the overrun probabilities in the objective function, wherein the objective of the fund path allocation model comprises maximizing benefits of the paymate and minimizing overrun probabilities of each first historical payment order, constraint conditions comprise the plurality of fund path level quota constraints, and decision variables comprise fund paths corresponding to each first historical payment order; and

Determining an output of the fund path allocation model, the output of the fund path allocation model configured to allocate a corresponding fund path for an order to be paid for any user of the paymate.

2. The method of claim 1, wherein the probability of an overrun payment comprises a predicted probability that a total amount of remaining payments exceeds a remaining user payment limit for the duration of the user-level payment limit constraint.

3. The method of claim 2, wherein the determining a plurality of overrun probabilities for each of the plurality of first historical payment orders comprises:

determining a corresponding plurality of overrun probabilities for the users to which each first historical payment order belongs based on a probability density function of the users to which each first historical payment order belongs,

wherein the probability density function of the user to which each first historical payment order belongs is obtained based on the following modes:

for each of the users to which the plurality of first historical payment orders belong, determining a daily payment amount distribution rule of the current user and a probability density function corresponding to the daily payment amount distribution rule of the current user based on the plurality of second historical payment orders of the current user.

4. The method of claim 3, wherein the determining the current user's daily payment amount distribution law and its corresponding probability density function based on the current user's second plurality of historical payment orders comprises:

and obtaining a total daily payment amount of any user in the time length of the second historical payment orders based on the second historical payment orders of any user, and obtaining a daily payment amount distribution rule of the corresponding user and a probability density function corresponding to the daily payment amount distribution rule by adopting an empirical distribution histogram mode.

5. A method as claimed in claim 3, wherein said determining, for each of said first historical payment orders, the corresponding plurality of overrun probabilities based on the probability density function of the user to which it belongs, comprises:

determining that after a current first historical payment order, a user to which the current first historical payment order belongs is the remaining user payment limit within the term of the user-level payment limit constraint;

and determining the overrun probabilities based on the probability density function of the user to which the current first historical payment order belongs and a plurality of preset quota occupation data, wherein the quota occupation data corresponds to the fund paths.

6. The method of claim 5, wherein the determining an objective function of a funds pathway allocation model based on the overrun probabilities corresponding to the plurality of first historical payment orders comprises:

and determining the result of dividing the gains of a plurality of fund paths corresponding to each first historical payment order and the overrun probability as the parameters of the decision variable of the objective function.

7. The method of claim 1, wherein the determining the output of the fund path allocation model comprises:

and solving the fund path distribution model based on an original dual algorithm to obtain a result of dual variables of the fund path distribution model.

8. The method of claim 7, further comprising:

distributing a target fund path for a target payment order from the plurality of fund paths based on the result of the dual variables and the overrun probability corresponding to the target payment order, wherein the target payment order is a payment order to be subjected to fund path distribution; and

and completing the target payment order through the target fund path.

9. The method of claim 8, wherein the assigning a target funds pathway to a target payment order from the plurality of funds pathways based on the results of the dual variables and the corresponding overrun probabilities of the target payment order comprises:

Constructing an objective function aiming at the objective payment order based on the result of the dual variables and the overrun probability corresponding to the objective payment order, wherein the objective of the objective function comprises maximizing the income of the objective payment order and minimizing the overrun probability corresponding to the objective payment order; and

the target funding pathway is determined based on the objective function.

10. A funds pathway processing system, comprising:

a server, the server comprising:

at least one storage medium storing at least one instruction set for performing allocation of a funds path; and

at least one processor communicatively coupled to the at least one storage medium,

wherein the at least one processor reads the at least one instruction set and performs the funds path processing method of any of claims 1-9 in accordance with the at least one instruction set when the funds path processing system is operating.