Information processing method, system, medium and computer system

Abstract

The present disclosure provides an information processing method, including: obtaining order information of a target order; determining multiple candidate coupons that can be used to reduce or exempt the payment amount when settling the target order based on the order information; determining the mutually exclusive relationship between two coupons in the multiple candidate coupons, wherein the coupons with the mutually exclusive relationship cannot be used at the same time; and outputting a target coupon combination whose discount amount meets a preset condition based on at least the mutually exclusive relationship and the multiple candidate coupons for use in settling the target order. The present disclosure also provides an information processing system, a computer system, and a computer-readable storage medium.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and more specifically, to an information processing method, an information processing system, a computer system, and a computer-readable storage medium. Background Art

[0002] In the Internet real economy, coupons are vouchers used to reduce or exempt the payment amount of goods. According to the form of vouchers, coupons can be divided into three categories: full-reduction coupons (for example, 5 yuan off for purchases over 100 yuan); direct reduction coupons (for example, direct reduction of 5 yuan); discount coupons (for example, 5% of the amount of the discounted order). According to the source, coupons can be divided into two categories: store coupons and platform coupons. Among them, hundreds of thousands of stores and merchants on the e-commerce platform can issue coupons for their respective store products; coupons issued by e-commerce platforms are generally divided into two categories: limited product category coupons and all product category coupons.

[0003] E-commerce platforms generate tens of thousands of orders every second. E-commerce platforms need to select and use the coupon or coupon combination with the largest discount for each online order. The difficulty of selecting the coupon combination with the largest discount lies in the following aspects:

[0004] (1) The calculation rules are complex. For example, each coupon has different conditions and different applicable scopes for different products. Each product can be discounted up to three times by combining three coupons of different categories. Since coupons are mutually exclusive and the amount is reduced, each coupon combination involves complex calculation logic.

[0005] (2) The amount of computation is large. The number of coupons applicable to each order is different because the coupons applied by the consumer are different and the items in the order are different. For example, if an order involves 40 applicable coupons, then the search space contains 2 40 There are more than 1 trillion different coupon combinations. In fact, an order may even involve hundreds of applicable coupons. For an ordinary order, finding the coupon combination with the largest calculated discount requires enumerating and comparing at least trillions of options.

[0006] (3) Processing time. The e-commerce platform business requires that for a single order, the optimal coupon selection problem must be calculated within a few milliseconds.

[0007] At present, the existing technical solution is a minimal enumeration method based on a huge search space. The program procedure is as follows: randomly assign a coupon (or a store coupon, or a limited category coupon, or a full category coupon) to each item in the order, and verify whether the solution is feasible, so as to obtain a coupon combination solution, and calculate the overall discount amount of the coupon combination solution; within the allowed computing time, repeat the above steps multiple times, record the largest overall discount amount, and retain its corresponding coupon combination solution; return the coupon combination solution that brings the largest overall discount amount.

[0008] In the process of realizing the concept of the present disclosure, the inventors found that the related art has at least the defects of poor calculation results, unreasonable calculation process and slow calculation speed. Summary of the invention

[0009] In view of this, the present disclosure provides an information processing method and system capable of providing a better coupon combination solution.

[0010] One aspect of the present disclosure provides an information processing method, including: obtaining order information of a target order; determining, based on the order information, a plurality of candidate coupons that can be used to reduce or exempt a payment amount when settling the target order; determining a mutually exclusive relationship between any two of the plurality of candidate coupons, wherein the coupons with a mutually exclusive relationship cannot be used at the same time; and outputting a target coupon combination whose discount amount meets a preset condition based on at least the mutually exclusive relationship and the plurality of candidate coupons for use in settling the target order.

[0011] According to an embodiment of the present disclosure, at least according to the mutually exclusive relationship, outputting a target coupon combination whose discount amount meets a preset condition based on the multiple candidate coupons includes: constructing an objective function for calculating the discount amount; setting an initial solution of the objective function, wherein the initial solution is an empty set; executing a preset first loop body when an iteration condition is satisfied, wherein the first loop body includes: selecting a candidate coupon from the multiple candidate coupons according to the mutually exclusive relationship and merging it into an initial coupon combination corresponding to the initial solution to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by an intermediate solution and the coupons contained therein are mutually exclusive; inputting the intermediate solution into the objective function and outputting a corresponding objective function value, wherein the objective function value represents the amount that can be discounted by using the intermediate coupon combination to settle the target order; using the intermediate solution as a new initial solution; and using the intermediate coupon combination as a new initial coupon combination; after the iteration is completed, extracting an objective function value that meets a preset value from all objective function values ​​obtained by executing the first loop body; and using the intermediate coupon combination corresponding to the objective function value as the target coupon combination.

[0012] According to an embodiment of the present disclosure, at least according to the mutually exclusive relationship, and based on the plurality of candidate coupons, outputting a target coupon combination whose discount amount meets a preset condition includes: constructing an objective function for calculating the discount amount; setting an initial solution of the objective function, wherein the initial solution is an empty set; executing a preset second loop body when an iteration condition is satisfied, wherein the second loop body includes: randomly selecting a candidate coupon from the plurality of candidate coupons; judging whether the candidate coupon and each coupon in the initial coupon combination corresponding to the initial solution satisfy the mutually exclusive relationship; if so, merging the candidate coupon into the initial coupon combination to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by an intermediate solution; inputting the intermediate solution into the objective function and outputting a corresponding objective function value, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order; using the intermediate solution as a new initial solution; and using the intermediate coupon combination as a new initial coupon combination; after the iteration ends, extracting an objective function value that satisfies a preset value from all objective function values ​​obtained by executing the second loop body; and using the intermediate coupon combination corresponding to the objective function value as the target coupon combination.

[0013] According to an embodiment of the present disclosure, in the second loop body: if the candidate coupons and the coupons in the initial coupon combination corresponding to the initial solution do not satisfy the mutually exclusive relationship, the candidate coupons are incorporated into the initial coupon combination and the coupons in the initial coupon combination that are mutually exclusive with the candidate coupons are deleted to obtain a corresponding intermediate coupon combination.

[0014] According to an embodiment of the present disclosure, the method further includes: determining the contribution value of each of the multiple candidate coupons to the discount amount when used alone to settle the target order; the method outputs a target coupon combination whose discount amount meets preset conditions at least based on the mutually exclusive relationship and the multiple candidate coupons, including: outputting a target coupon combination whose discount amount meets the preset conditions based on the multiple candidate coupons and the size of the mutually exclusive relationship and the contribution value.

[0015] According to an embodiment of the present disclosure, the above-mentioned determining, based on the above-mentioned order information, a plurality of candidate coupons that can be used to reduce or exempt the payment amount when settling the above-mentioned target order includes: extracting user information and object information from the above-mentioned order information, wherein the above-mentioned user information corresponds to a target user associated with the above-mentioned target order, and the above-mentioned object information corresponds to a transaction object included in the above-mentioned target order; determining, based on the above-mentioned user information, all coupons that can be used by the above-mentioned target user; and selecting, based on the above-mentioned object information, coupons that can be used by the above-mentioned transaction object from all the above-mentioned coupons to obtain the above-mentioned plurality of candidate coupons.

[0016] According to an embodiment of the present disclosure, determining the mutually exclusive relationship between any two of the multiple candidate coupons includes: acquiring a usage rule of each coupon in the multiple candidate coupons; and determining the mutually exclusive relationship between any two of the multiple candidate coupons based on the usage rule.

[0017] Another aspect of the present disclosure provides an information processing system, including: an acquisition module for acquiring order information of a target order; a first determination module for determining, based on the order information, a plurality of candidate coupons that can be used to reduce or exempt a payment amount when settling the target order; a second determination module for determining a mutually exclusive relationship between any two of the plurality of candidate coupons, wherein coupons with a mutually exclusive relationship cannot be used at the same time; and an output module for outputting a target coupon combination whose discount amount meets preset conditions based on at least the mutually exclusive relationship and the plurality of candidate coupons, for use in settling the target order.

[0018] According to an embodiment of the present disclosure, the output module includes: a first construction unit, which is used to construct an objective function for calculating the discount amount; a first setting unit, which is used to set an initial solution of the objective function, wherein the initial solution is an empty set; a first processing unit, which is used to execute a preset first loop body when an iteration condition is met, wherein the first loop body includes: according to the mutually exclusive relationship, selecting a candidate coupon from the multiple candidate coupons and merging it into an initial coupon combination corresponding to the initial solution to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by an intermediate solution and the coupons contained therein are mutually exclusive; inputting the intermediate solution into the objective function and outputting the corresponding objective function value, wherein the objective function value represents the amount that can be discounted by using the intermediate coupon combination to settle the target order; using the intermediate solution as a new initial solution; and using the intermediate coupon combination as a new initial coupon combination; a first extraction unit, which is used to extract a target function value that meets a preset value from all target function values ​​obtained by executing the first loop body after the iteration ends; and a first determination unit, which is used to use the intermediate coupon combination corresponding to the target function value as the target coupon combination.

[0019] According to an embodiment of the present disclosure, the output module includes: a second construction unit, which is used to construct an objective function for calculating the discount amount; a second setting unit, which is used to set an initial solution of the objective function, wherein the initial solution is an empty set; a second processing unit, which is used to execute a preset second loop body when an iteration condition is met, wherein the second loop body includes: randomly selecting a candidate coupon from the multiple candidate coupons; judging whether the candidate coupon and each coupon in the initial coupon combination corresponding to the initial solution satisfy the mutually exclusive relationship; if satisfied, merging the candidate coupon into the initial coupon combination to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by an intermediate solution; inputting the intermediate solution into the objective function and outputting a corresponding objective function value, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order; using the intermediate solution as a new initial solution; and using the intermediate coupon combination as a new initial coupon combination; a second extraction unit, which is used to extract an objective function value that satisfies a preset value from all objective function values ​​obtained by executing the second loop body after the iteration ends; and a second determination unit, which is used to use the intermediate coupon combination corresponding to the objective function value as the target coupon combination.

[0020] According to an embodiment of the present disclosure, in the second loop body: if the candidate coupons and the coupons in the initial coupon combination corresponding to the initial solution do not satisfy the mutually exclusive relationship, the candidate coupons are incorporated into the initial coupon combination and the coupons in the initial coupon combination that are mutually exclusive with the candidate coupons are deleted to obtain a corresponding intermediate coupon combination.

[0021] According to an embodiment of the present disclosure, the system further includes: a third determination module, used to determine the contribution value of each of the multiple candidate coupons to the discount amount when the coupon is used alone to settle the target order; the output module is also used to: output a target coupon combination whose discount amount meets the preset conditions based on the multiple candidate coupons according to the mutually exclusive relationship and the size of the contribution value.

[0022] According to an embodiment of the present disclosure, the above-mentioned first determination module includes: a third extraction unit, used to extract user information and object information from the above-mentioned order information, wherein the above-mentioned user information corresponds to a target user associated with the above-mentioned target order, and the above-mentioned object information corresponds to a transaction object contained in the above-mentioned target order; a third determination unit, used to determine all coupons that can be used by the above-mentioned target user based on the above-mentioned user information; and a selection unit, used to select coupons that can be used by the above-mentioned transaction object from all the above-mentioned coupons based on the above-mentioned object information, to obtain the above-mentioned multiple candidate coupons.

[0023] According to an embodiment of the present disclosure, the second determination module includes: an acquisition unit, configured to acquire a usage rule of each coupon in the plurality of candidate coupons; and a fourth determination unit, configured to determine the mutually exclusive relationship between any two coupons in the plurality of candidate coupons based on the usage rule.

[0024] Another aspect of the present disclosure provides a computer system, including: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0025] Another aspect of the present disclosure provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method described above.

[0026] Another aspect of the present disclosure provides a computer program, wherein the computer program includes computer executable instructions, and the instructions are used to implement the above method when executed.

[0027] The disclosed embodiments accelerate the search speed of meta-heuristic search and avoid wasting computing time by developing an artificial intelligence search algorithm (based on a mutually exclusive matrix). Even in a huge search space, it is possible to accurately calculate how to select coupons for dynamically given product orders to achieve the maximum discount. This greatly improves the computing speed while ensuring accurate calculations, achieving the goal of completing all calculations within a few milliseconds. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0029] Figure 1 Schematically illustrates an exemplary system architecture to which the information processing method and system of the present disclosure can be applied;

[0030] Figure 2 The application scenario of the information processing method according to the embodiment of the present disclosure is schematically shown;

[0031] Figure 3 The flowchart of the information processing method according to the embodiment of the present disclosure is schematically shown;

[0032] Figure 4 The loop logic of a loop body according to an embodiment of the present disclosure is schematically shown;

[0033] Figure 5 Schematically illustrates the loop logic of another loop body according to an embodiment of the present disclosure;

[0034] Figure 6 The intelligent ticket selection process according to an embodiment of the present disclosure is schematically shown;

[0035] Figure 7 A flowchart schematically shows an information processing method according to other embodiments of the present disclosure;

[0036] Figure 8 A flowchart for determining candidate coupons according to an embodiment of the present disclosure is schematically shown;

[0037] Fig. 9 A flowchart schematically illustrates a mutually exclusive relationship of coupons according to an embodiment of the present disclosure;

[0038] Fig.10 A block diagram schematically shows an information processing system according to an embodiment of the present disclosure;

[0039] Fig.11 A block diagram of an output module according to an embodiment of the present disclosure is schematically shown;

[0040] Fig.12 A block diagram schematically shows an output module according to other embodiments of the present disclosure; and

[0041] Fig.13 A block diagram of a computer system suitable for implementing an information processing method and system according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0042] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0043] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0044] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0045] In the case of using expressions such as "at least one of A, B, and C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but not be limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). In the case of using expressions such as "at least one of A, B, or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, or C" should include but not be limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). Those skilled in the art should also understand that any transitional conjunctions and / or phrases that substantially indicate two or more optional items, whether in the specification, claims, or drawings, should be understood to give the possibility of including one of these items, either of these items, or both of these items. For example, the phrase "A or B" should be understood to include the possibilities of "A" or "B", or "A and B".

[0046] The embodiment of the present disclosure provides an information processing method and an information processing system for implementing the method. The method includes obtaining order information of a target order; determining multiple candidate coupons that can be used to reduce or exempt the payment amount when settling the target order based on the order information; determining the mutually exclusive relationship between two coupons in the multiple candidate coupons, wherein the coupons with mutually exclusive relationships cannot be used at the same time; and outputting a target coupon combination whose discount amount meets a preset condition based on at least the mutually exclusive relationship and multiple candidate coupons for use in settling the target order.

[0047] Figure 1 The exemplary system architecture to which the information processing method and system of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0048] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0049] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only as examples).

[0050] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0051] The server 105 may be a server that provides various services, such as a backend server that provides settlement services for orders submitted by users using the terminal devices 101, 102, and 103 (for example only). The backend server may analyze and process the received order data, and feed back the processing results (such as generating the optimal coupon combination plan and the final settlement result based on the user's order) to the terminal device.

[0052] It should be noted that the information processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the information processing system provided in the embodiment of the present disclosure can generally be set in the server 105. The information processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the information processing system provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0053] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0054] Figure 2 The application scenario of the information processing method according to the embodiment of the present disclosure is schematically shown. It should be noted that: Figure 2 The examples shown are merely examples of application scenarios to which the embodiments of the present disclosure can be applied, so as to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure cannot be used in other scenarios.

[0055] like Figure 2As shown, the consumer places an order through the client and sends the order information (including user information and product information in the order shopping cart) to the e-commerce platform order settlement server through a web request. After receiving the request, the order settlement server assigns the order to a coupon server in the coupon management server cluster. After receiving the order, the coupon server queries the coupon storage Redis cluster for the available coupons for this order and the usage rules of these coupons based on the order information, and selects a coupon combination plan that meets the conditions from the available coupons through the intelligent coupon selection unit.

[0056] The information processing method provided by the present disclosure can help e-commerce platforms select better or even optimal coupon combination solutions from available coupons. It should be understood that the information processing method provided by the present disclosure can also adapt to the situation where users repeatedly modify orders and repeatedly submit orders. Figure 2 As shown, the consumer may modify the order multiple times and repeat steps 2-4 multiple times to determine the final purchased goods, and then determine the final discount amount and payment amount. The coupon server then notifies the order settlement server and pushes it to the consumer. The consumer accepts the final goods settlement amount and pays the order.

[0057] Figure 3 The flowchart of the information processing method according to the embodiment of the present disclosure is schematically shown.

[0058] like Figure 3 As shown, the method includes operations S302 to S308.

[0059] In operation S302, order information of the target order is obtained.

[0060] Specifically, the information processing method provided by the present disclosure is executed by a server in a coupon server cluster, such as Figure 2 As shown, these coupon servers receive orders assigned by the e-commerce platform order settlement server and read the corresponding order information from the order. It should be understood that order information includes but is not limited to user information (i.e. consumer information, such as whether it is an ordinary user or a member, etc.) and object information (i.e. product information, such as the category and quantity of the product, etc.).

[0061] In operation S304, a plurality of candidate coupons that can be used to reduce or waive the payment amount when settling the target order are determined based on the order information.

[0062] Specifically, all candidate coupons suitable for the target order can be determined from two dimensions: consumers and commodities involved in the order.

[0063] In operation S306, mutually exclusive relationships between coupons in the plurality of candidate coupons are determined, wherein the coupons with mutually exclusive relationships cannot be used at the same time.

[0064] Specifically, for candidate coupons, a coupon mutual exclusion matrix, i.e., a Cartesian product two-dimensional matrix, can be established based on the application conditions, i.e., usage rules, of the coupons to record the mutual exclusion relationship between each coupon and another coupon. Specifically, in the mutual exclusion matrix, "1" can be used to indicate that two coupons are mutually exclusive, and "0" can be used to indicate that two coupons are not mutually exclusive, or vice versa, "0" can be used to indicate that two coupons are mutually exclusive, and "1" can be used to indicate that two coupons are not mutually exclusive. The mutual exclusion matrix greatly speeds up the search process and simplifies the calculation of constraint conditions.

[0065] In operation S308, a target coupon combination whose discount amount meets a preset condition is output based on at least the mutually exclusive relationship and multiple candidate coupons for use in settling the target order.

[0066] Specifically, you can first obtain multiple coupon combinations, and then select the most favorable coupon combination for order settlement.

[0067] Through the embodiments of the present disclosure, a technical means of selecting the best coupons based on artificial intelligence is adopted, so that the system can automatically recommend the most favorable coupon combination when the user submits an order, thereby ensuring that consumers can obtain the maximum coupon shopping discount and an excellent shopping experience.

[0068] Because the existing technology can only search a small part of the huge search space, and the coupon combination scheme returned by the enumeration method is far different from the actual coupon combination scheme with the maximum discount, especially for slightly larger problems (such as 6 coupons), the existing technical solutions are sometimes worse than the coupon combination schemes found by consumers with naked eyes. Compared with the poor calculation results of the prior art, the embodiment of the present disclosure accelerates the search speed of the meta-heuristic search and avoids the waste of computing time by developing an artificial intelligence search algorithm (based on a mutually exclusive matrix). Even in a huge search space, it can accurately calculate how to select coupons for dynamically given commodity orders to achieve the maximum discount, thereby greatly improving the computing speed while ensuring accurate calculations, and achieving the purpose of completing all calculations within a few milliseconds.

[0069] Reference below Figures 4 to 9 , combined with specific embodiments Figure 3 The method shown is further explained.

[0070] In the embodiment of the present disclosure, operation S308 may be implemented by at least two iterative algorithms, that is, outputting a target coupon combination whose discount amount meets a preset condition based on at least a mutually exclusive relationship and a plurality of candidate coupons.

[0071] An iterative algorithm such as Figure 4As shown, operation S308 outputs a target coupon combination whose discount amount meets the preset conditions based on at least the mutually exclusive relationship and multiple candidate coupons, including: constructing an objective function for calculating the discount amount; setting an initial solution of the objective function, wherein the initial solution is an empty set; executing a preset first loop body when the iteration condition is met; after the iteration is completed, extracting an objective function value that meets the preset value from all objective function values ​​obtained by executing the first loop body; and using the intermediate coupon combination corresponding to the objective function value as the target coupon combination. The first loop body includes:

[0072] In operation S402, a candidate coupon is selected from the plurality of candidate coupons according to the mutually exclusive relationship and is incorporated into the initial coupon combination corresponding to the initial solution to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by the intermediate solution and the coupons contained therein are mutually exclusive;

[0073] In operation S404, the intermediate solution is input into the objective function and the corresponding objective function value is output, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order;

[0074] In operation S406, the intermediate solution is used as a new initial solution; and

[0075] In operation S408, the intermediate coupon combination is used as a new initial coupon combination.

[0076] Another iterative algorithm is Figure 5 As shown, operation S308 outputs a target coupon combination whose discount amount meets the preset conditions based on at least the mutually exclusive relationship and multiple candidate coupons, including: constructing an objective function for calculating the discount amount; setting an initial solution of the objective function, wherein the initial solution is an empty set; executing a preset second loop body when the iteration condition is met; after the iteration is completed, extracting an objective function value that meets the preset value from all objective function values ​​obtained by executing the second loop body; and using the intermediate coupon combination corresponding to the objective function value as the target coupon combination. The second loop body includes:

[0077] In operation S502, a candidate coupon is randomly selected from a plurality of candidate coupons;

[0078] In operation S504, it is determined whether the candidate coupons and the coupons in the initial coupon combination corresponding to the initial solution satisfy a mutually exclusive relationship;

[0079] In operation S506, if the conditions are met, the candidate coupon is merged into the initial coupon combination to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by an intermediate solution;

[0080] In operation S508, the intermediate solution is input into the objective function and the corresponding objective function value is output, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order;

[0081] In operation S510, the intermediate solution is used as a new initial solution; and

[0082] In operation S512, the intermediate coupon combination is used as a new initial coupon combination;

[0083] Further, as an optional embodiment, Figure 5 As shown, in the second loop body:

[0084] In operation S514, if the candidate coupons and the coupons in the initial coupon combination corresponding to the initial solution do not satisfy a mutually exclusive relationship, the candidate coupons are incorporated into the initial coupon combination and the coupons in the initial coupon combination that are mutually exclusive with the candidate coupons are deleted to obtain a corresponding intermediate coupon combination.

[0085] The existing technology has a slow calculation speed because the enumeration method is an inefficient solution. The existing technology solution can only use 100% of its allocated running time, but the business logic hopes to calculate the correct result in less time.

[0086] Through the above embodiments, two loop iteration logics are used to provide a technical implementation method for the optimal coupon for order settlement, which reduces the consumer's order expenditure to the minimum. The simulation experiment shows that the optimal coupon technology saves consumers several percentage points of expenditure. The optimal coupon technology promotes the conversion rate of consumer orders, increases the utilization rate of coupons, improves the marketing of e-commerce, and greatly improves the calculation speed (within several milliseconds) under the premise of ensuring accurate calculation.

[0087] As an optional embodiment, Figure 7 As shown, the method also includes:

[0088] In operation S702, determining a contribution value of each coupon among the plurality of candidate coupons to the discount amount when used alone to settle the target order;

[0089] In operation S308, at least based on the mutually exclusive relationship, a target coupon combination whose discount amount meets the preset conditions is output based on multiple candidate coupons, including:

[0090] In operation S704, a target coupon combination whose discount amount meets a preset condition is output based on the mutual exclusive relationship and the size of the contribution value and on the basis of a plurality of candidate coupons.

[0091] The calculation process of the prior art is unreasonable. Since the coupon combination scheme of the prior art is randomly generated, for each coupon combination scheme, only the constraint conditions and discount amount of each coupon corresponding to each product can be fully calculated.

[0092] Through the embodiments of the present disclosure, for a given order, the method for calculating the coupon usage plan no longer performs full calculations for each coupon corresponding to each commodity, but only performs incremental calculations on the transformed part, thereby greatly reducing the amount of calculations and improving the speed.

[0093] As an optional embodiment, Figure 8 As shown, operation S304 determines, based on the order information, multiple candidate coupons that can be used to reduce or exempt the payment amount when settling the target order, including:

[0094] In operation S802, user information and object information are extracted from the order information, wherein the user information corresponds to a target user associated with the target order, and the object information corresponds to a transaction object included in the target order;

[0095] In operation S804, all coupons that the target user can use are determined based on the user information; and

[0096] In operation S806, coupons available for use by the transaction object are selected from all coupons according to the object information to obtain a plurality of candidate coupons.

[0097] Through the embodiments of the present disclosure, multiple candidate coupons can be accurately locked and a reasonable search space can be found.

[0098] As an optional embodiment, Fig. 9 As shown, operation S306 determines the mutually exclusive relationship between two coupons in the plurality of candidate coupons, including:

[0099] In operation S902, obtaining usage rules of each coupon among a plurality of candidate coupons; and

[0100] In operation S904, the mutually exclusive relationship between any two coupons in the plurality of candidate coupons is determined according to the usage rule.

[0101] Through the embodiments of the present disclosure, the mutually exclusive relationship between coupons can be accurately determined, thereby providing a basis for improving calculation efficiency.

[0102] Specifically, the coupon server transmits information such as the goods in the order, the coupon applicable to the consumer, and the applicable conditions of the coupon as input parameters to the coupon intelligent selection unit, and the coupon intelligent selection unit can select the coupon combination according to the following operations:

[0103] Step 1: According to the input parameters, a mathematical model is established through the initialization unit, data is prepared, and an initial solution (i.e., a coupon usage plan) is constructed;

[0104] Step 2, input the initial solution, and cyclically call the generation unit, the calculation unit, and the evaluation unit according to the first iterative algorithm, and iterate the Max number of times to improve the quality of the initial solution;

[0105] Step 3: extract the most favorable coupon combination through the output unit, convert the data format of the coupon combination, and then return the selection result.

[0106] Combine the following Figure 6 Detailed description of the main components of the coupon intelligent selection unit and their relationships:

[0107] (1) Initialization unit

[0108] The initialization unit is used to complete the following three tasks:

[0109] Task 1: According to the relationship between the incoming products and the available coupons, establish a mathematical model for the coupon selection problem for the target order, as shown in Formula 1:

[0110] Formula 1:

[0111]

[0112] in:

[0113] Formula 2:

[0114]

[0115] Formula 3:

[0116]

[0117] Formula 4:

[0118]

[0119] Formula 5:

[0120]

[0121] Formula 6:

[0122]

[0123] Formula 7:

[0124]

[0125] The variables are:

[0126] S is the set of different SKU products in the order;

[0127] D is the set of store coupons that can be selected in the order;

[0128] X is the set of category-limited coupons that can be selected in the order;

[0129] Q is a collection of all-category coupons that can be selected in the order;

[0130] a ij =1, if for SKU product s i Coupon j is allowed to be used; otherwise a ij =0;

[0131] u ij =1, if for SKU product s i Coupon j is finally applied; otherwise u ij =0;

[0132] d j , x j ,q j It is the discount amount of the corresponding store type coupon, limited category coupon, and all category coupon;

[0133] is the amount that meets the conditions for applying coupon j (the amount of the product reaches the coupon discount condition);

[0134] is the sales amount of SKU product s;

[0135] t ij =1 if the jth store coupon can be applied to the ith SKU product; otherwise 0;

[0136] y ij =1 if the jth limited category coupon can be applied to the ith SKU product; otherwise 0;

[0137] z ij =1 if the jth full-category coupon can be applied to the ith SKU product; otherwise, 0;

[0138] U matrix corresponds to u ij defines the solution to this problem. For a given order, the solution defines which

[0139] Store type, category-limited and all-category coupons can be used for which products;

[0140] Function f apportion () Calculates the discount amount shared by the coupon on the current solution. The definition of this function depends on the specific business settlement logic.

[0141] The mathematical model above defines the following:

[0142] Formula 1 defines a solution for any given problem: how to calculate the quality of the solution, i.e., the objective function value (the sum of the discount amounts of a coupon usage scheme);

[0143] Formula 1 also defines the optimization direction of the mathematical model, that is, the larger the value of the objective function, the better (for consumers, the larger the discount amount, the better);

[0144] Formula 2-4 defines the constraint that a coupon for a category can only be applied to one product at most;

[0145] Formula 5-7 defines the conditions for using the coupon (the amount of the product reaches the coupon discount condition), which can be superimposed and used together to ensure that the solution can meet all constraints.

[0146] The above formula is a full calculation plan, because it means calculating all the products and coupons involved in an order one by one.

[0147] If some of the summation formulas in Formula 1-7 are removed, Formula 1 can also perform incremental calculations element by element (each coupon), that is, solve for the individual discount amount contribution value of each coupon to the entire order.

[0148] For a given store coupon j, its contribution to the objective function of the current solution U can be obtained from Formula 8:

[0149] Formula 8:

[0150]

[0151] For a given limited category coupon j, its contribution to the objective function of the current solution U can be obtained from Formula 9:

[0152] Formula 9:

[0153]

[0154] For a given full-category coupon j, its contribution to the objective function of the current solution U can be obtained from Formula 10:

[0155] Formula 10:

[0156]

[0157] Task 2, based on the application conditions of the coupons, establish a coupon mutual exclusion matrix. A two-dimensional matrix for the Cartesian product of coupons. Based on the applicable product range of the coupons, the coupon mutual exclusion matrix records the mutual exclusion relationship between each coupon and another coupon. The mutual exclusion matrix greatly speeds up the search process and simplifies the calculation of the constraints.

[0158] Task 3, construct the initial solution and gain matrix to establish the initial solution, and establish the gain matrix for the elements in the solution. The initial solution is achieved by the following steps:

[0159] Step 1, set the initial solution to an empty set, that is, no coupon is used.

[0160] Step 2, first construct the gain matrix. The gain matrix is ​​a one-dimensional array for all coupons. It records the individual discount amount contribution of each coupon to the entire order in the initial solution. The use of the gain matrix greatly reduces the amount of calculation of the objective function value in the following calculation unit.

[0161] Step 3: For each product in the order, if the coupon usage conditions are met (the product amount reaches the coupon discount condition, i.e., Formula 5) and the coupon usage mutual exclusion constraint conditions are met (using the coupon mutual exclusion matrix), randomly assign at most one store coupon to the product from the candidate store coupons for the product.

[0162] Step 4: For each product in the order, under the premise that the coupon stacking conditions are met, the coupon usage conditions (i.e., Formula 6) are met, and the coupon usage mutually exclusive constraints are met, randomly allocate at most one limited category coupon to the product from the candidate limited category coupons for the product.

[0163] Step 5: For each product in the order, under the premise that the coupon stacking conditions, coupon usage conditions, and coupon usage mutually exclusive constraints are met (i.e., Formula 7), randomly allocate at most one all-category coupon to the product from the candidate all-category coupons for the product.

[0164] In step 3-5, if a coupon is successfully added to the initial solution, the corresponding element on the gain matrix is ​​updated with the discount amount of the coupon (by using formulas 8-10).

[0165] After the initial solution is constructed, the coupon intelligent selection unit iteratively calls the generation unit, calculation unit and evaluation unit to gradually improve the intermediate solution of the current order problem. In each iteration, there are the following steps:

[0166] Step 1: Based on the intermediate solution of the previous iteration (the initial solution is the intermediate solution of the first iteration), the generation unit generates a temporary intermediate solution;

[0167] Step 2, the calculation unit calculates the objective function value of the temporary new solution;

[0168] Step 3, the evaluation unit determines whether the temporary intermediate solution can be accepted as the intermediate solution;

[0169] Step 4: If acceptable, continue to the next iteration with the intermediate solution;

[0170] Step 5: In the last iteration, the best intermediate solution found in the whole process is taken as the final solution.

[0171] (2) Generation unit

[0172] The input of the generation unit is the initial solution. The generation unit continuously transforms the intermediate solution in the iteration through the following three tasks to generate a new solution.

[0173] Task 1, add coupons:

[0174] Step 1: Build a candidate set of coupons to be added, and select unused coupons on the current intermediate solution (e.g., u ij =0).

[0175] Step 2: Further filter the candidate coupon set to be added, and remove all coupons that are exclusive with the coupons being used in the current solution (using the coupon mutual exclusion matrix). The use of the coupon mutual exclusion matrix ensures that the new temporary intermediate solution meets the constraints, thereby reducing the running time.

[0176] Step 3: If the candidate coupon set is not empty, select any coupon from the final candidate coupon set. The selected coupon is the addable coupon, and this coupon can be added to the current intermediate solution.

[0177] Step 4: If the candidate coupon set is empty (i.e., adding coupon fails), the coupon is transformed.

[0178] Task 2, minus coupons:

[0179] To transform coupons, in order to add a coupon, you need to subtract a number of coupons from the current solution. Transforming coupons includes the following steps:

[0180] Step 1: Construct a candidate set of coupons that can be added, select unused coupons in the current intermediate solution, and select any coupon from the candidate set of coupons. This coupon is the coupon that can be added.

[0181] Step 2: Use the coupon mutual exclusion matrix to find all coupons that are being used on the current intermediate solution and are mutually exclusive with the selected coupon. These coupons constitute the removed coupon set.

[0182] Task 3, perform the transformation:

[0183] Step 1: Copy the intermediate solution to form a new temporary intermediate solution.

[0184] Step 2: remove the coupons in the removed coupon set from the temporary intermediate solution.

[0185] Step 3: Add the addable coupon to the temporary intermediate solution, that is, change the current intermediate solution by adding coupons or exchanging coupons to generate a temporary intermediate solution.

[0186] (3) Computing unit

[0187] The task of the calculation unit is to calculate the objective function value of the temporary intermediate solution. The calculation unit does not calculate the constraints and discount amounts for all commodities and coupons one by one for the temporary intermediate solution, but calculates the gain brought by the temporary intermediate solution to the objective function based on the difference between the current intermediate solution and the temporary intermediate solution (i.e., different coupons used), thereby inferring the total objective function value of the temporary intermediate solution. The calculation unit includes three tasks:

[0188] Task 1, add the objective function value of the coupon. For the newly added store type, limited category, or full category coupon, use formula 8-10 to calculate the contribution of the coupon to the objective function value of the solution.

[0189] Task 2, subtract the objective function value of the coupon. For the store type, limited category, or full category coupon to be subtracted from the solution, the objective function contribution value (i.e., coupon amount) of the transformation element (the coupon to be subtracted) is extracted based on the gain matrix.

[0190] Task 3, calculate the objective function value of the new intermediate solution. That is, calculate the difference between the objective function value of the new intermediate solution and the objective function value minus the coupon. Only the transformation elements (a single or a few coupons) are calculated individually, and by using the gain matrix, a lot of computing time can be greatly reduced.

[0191] The model introduced by this technical solution is suitable for full-scale and incremental calculations, which greatly speeds up the calculation speed.

[0192] (4) Evaluation unit

[0193] The evaluation unit determines whether the temporary intermediate solution can be used to update the current intermediate solution based on the objective function value of the temporary intermediate solution. If so, the update is completed. The evaluation unit includes the following tasks:

[0194] Task 1: If the objective function value of the temporary intermediate solution is better than the objective function value of the current intermediate solution, the evaluation unit will accept the temporary intermediate solution; if not, the evaluation unit can determine whether the temporary intermediate solution is acceptable according to an artificial intelligence meta-heuristic search algorithm, such as Simulated Annealing and its various variant algorithms, Tabu Search, or Great Deluge;

[0195] Task 2: The best solution found in the loop is recorded, saved and updated. If the objective function value of the temporary intermediate solution is better than the objective function value of the best solution recorded, the temporary intermediate solution becomes the best solution.

[0196] Task 3, if the temporary intermediate solution is accepted by the evaluation unit, the individual objective function contribution value involving the transformed coupon on the gain matrix is ​​updated;

[0197] Task 4: Update the algorithm parameter values ​​according to the requirements of the artificial intelligence metaheuristic search algorithm, such as the acceptance probability of the worse solution (temperature parameter in Simulated Annealing) and the acceptance probability of the regional solution of Tabu Search.

[0198] (5) Output unit

[0199] The output unit extracts the best solution found in the loop process, converts the matrix U in the best solution into a coupon usage plan, and finally returns the result to the order settlement server.

[0200] The following algorithms should be understood: Simulated Annealing: Simulated Annealing is a modern meta-heuristic algorithm used to find the optimal solution in a large search space in a fixed time. Tabu Search: Tabu Search is a modern meta-heuristic algorithm, a search method used to escape from the local optimal solution; Great Deluge: The Great Deluge algorithm is a general algorithm applied to optimization problems. It is similar to hill climbing and simulated annealing algorithms in many ways.

[0201] Fig.10 The block diagram schematically shows an information processing system according to an embodiment of the present disclosure.

[0202] like Fig.10 As shown, the information processing system 100 includes: an acquisition module 110 , a first determination module 120 , a second determination module 130 and an output module 140 .

[0203] An acquisition module 110 is used to acquire order information of a target order; a first determination module 120 is used to determine, based on the order information, a plurality of candidate coupons that can be used to reduce or exempt the payment amount when settling the target order; a second determination module 130 is used to determine the mutually exclusive relationship between any two of the plurality of candidate coupons, wherein coupons with a mutually exclusive relationship cannot be used at the same time; and an output module 140 is used to output a target coupon combination whose discount amount meets preset conditions based on at least the mutually exclusive relationship and the plurality of candidate coupons, for use in settling the target order.

[0204] Through the embodiments of the present disclosure, by developing an artificial intelligence search algorithm (based on a mutually exclusive matrix), the search speed of the meta-heuristic search is accelerated to avoid wasting computing time. Even in a huge search space, it is possible to accurately calculate how to select coupons for dynamically given product orders to achieve the maximum discount, thereby greatly improving the computing speed while ensuring accurate calculations, and achieving the goal of completing all calculations within a few milliseconds.

[0205] According to the embodiments of the present disclosure, Fig.11 As shown, the output module 140 includes: a first construction unit 141, which is used to construct an objective function for calculating the discount amount; a first setting unit 142, which is used to set the initial solution of the objective function, wherein the initial solution is an empty set; a first processing unit 143, which is used to execute a preset first loop body when the iteration condition is met; a first extraction unit 144, which is used to extract an objective function value that meets the preset value from all objective function values ​​obtained by executing the first loop body after the iteration ends; and a first determination unit 145, which is used to use the intermediate coupon combination corresponding to the objective function value as the target coupon combination. Wherein, the first loop body includes: according to the mutually exclusive relationship, a candidate coupon is selected from multiple candidate coupons and merged into the initial coupon combination corresponding to the initial solution to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by the intermediate solution and the coupons contained therein are mutually exclusive; inputting the intermediate solution into the objective function and outputting the corresponding objective function value, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order; using the intermediate solution as a new initial solution; and using the intermediate coupon combination as a new initial coupon combination.

[0206] According to the embodiments of the present disclosure, Fig.12As shown, the output module 140 includes: a second construction unit 146, which is used to construct an objective function for calculating the discount amount; a second setting unit 147, which is used to set an initial solution of the objective function, wherein the initial solution is an empty set; a second processing unit 148, which is used to execute a preset second loop body when an iteration condition is met; a second extraction unit 149, which is used to extract an objective function value that meets a preset value from all objective function values ​​obtained by executing the second loop body after the iteration ends; and a second determination unit 1410, which is used to use the intermediate coupon combination corresponding to the objective function value as the target coupon combination. The second loop body includes: randomly selecting a candidate coupon from multiple candidate coupons; judging whether the candidate coupon satisfies a mutually exclusive relationship with each coupon in the initial coupon combination corresponding to the initial solution; if so, merging the candidate coupon into the initial coupon combination to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by the intermediate solution; inputting the intermediate solution into the objective function and outputting the corresponding objective function value, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order; using the intermediate solution as a new initial solution; and using the intermediate coupon combination as a new initial coupon combination.

[0207] According to an embodiment of the present disclosure, in the second loop body: if the candidate coupons and the coupons in the initial coupon combination corresponding to the initial solution do not satisfy a mutually exclusive relationship, the candidate coupons are incorporated into the initial coupon combination and the coupons in the initial coupon combination that are mutually exclusive with the candidate coupons are deleted to obtain a corresponding intermediate coupon combination.

[0208] Through the above embodiments, two loop iteration logics are used to provide a technical implementation method for the optimal coupon for order settlement, which reduces the consumer's order expenditure to the minimum. The simulation experiment shows that the optimal coupon technology saves consumers several percentage points of expenditure. The optimal coupon technology promotes the conversion rate of consumer orders, increases the utilization rate of coupons, improves the marketing of e-commerce, and greatly improves the calculation speed (within several milliseconds) under the premise of ensuring accurate calculation.

[0209] According to an embodiment of the present disclosure, the system also includes: a third determination module, which is used to determine the contribution value of each coupon among multiple candidate coupons to the discount amount when it is used alone to settle the target order; the output module is also used to: output a target coupon combination whose discount amount meets preset conditions based on the multiple candidate coupons according to the mutually exclusive relationship and the size of the contribution value.

[0210] Through the embodiments of the present disclosure, for a given order, the method for calculating the coupon usage plan no longer performs full calculations for each coupon corresponding to each commodity, but only performs incremental calculations on the transformed part, thereby greatly reducing the amount of calculations and improving the speed.

[0211] According to an embodiment of the present disclosure, the first determination module includes: a third extraction unit, used to extract user information and object information from the order information, wherein the user information corresponds to a target user associated with a target order, and the object information corresponds to a transaction object contained in the target order; a third determination unit, used to determine all coupons that can be used by the target user based on the user information; and a selection unit, used to select coupons that can be used by the transaction object from all coupons based on the object information, to obtain multiple candidate coupons.

[0212] Through the embodiments of the present disclosure, multiple candidate coupons can be accurately locked and a reasonable search space can be found.

[0213] According to an embodiment of the present disclosure, the second determination module includes: an acquisition unit, configured to acquire a usage rule of each coupon among multiple candidate coupons; and a fourth determination unit, configured to determine a mutually exclusive relationship between any two coupons among the multiple candidate coupons according to the usage rule.

[0214] Through the embodiments of the present disclosure, the mutually exclusive relationship between coupons can be accurately determined, thereby providing a basis for improving calculation efficiency.

[0215] According to the modules and units of the embodiments of the present disclosure, any multiple or at least part of the functions of any multiple thereof can be implemented in one module. According to the modules and units of the embodiments of the present disclosure, any one or more can be split into multiple modules for implementation. According to the modules and units of the embodiments of the present disclosure, any one or more can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware of any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, according to the modules and units of the embodiments of the present disclosure, one or more can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0216] For example, any multiple of the acquisition module 110, the first determination module 120, the second determination module 130, and the output module 140 can be combined in one module / unit for implementation, or any one of the modules / units can be split into multiple modules / units. Alternatively, at least part of the functions of one or more of these modules / units / subunits can be combined with at least part of the functions of other modules / units / subunits and implemented in one module / unit. According to an embodiment of the present disclosure, at least one of the acquisition module 110, the first determination module 120, the second determination module 130, and the output module 140 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware, and firmware, or by a suitable combination of any of them. Alternatively, at least one of the acquisition module 110 , the first determination module 120 , the second determination module 130 and the output module 140 may be at least partially implemented as a computer program module, and when the computer program module is executed, a corresponding function may be performed.

[0217] It should be noted that the information processing system part in the embodiments of the present disclosure corresponds to the information processing method part in the embodiments of the present disclosure. The description of the information processing system part specifically refers to the information processing method part, which will not be repeated here.

[0218] Fig.13 A block diagram of a computer system suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Fig.13 The computer system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0219] like Fig.13 As shown, the computer system 500 according to the embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 to the random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0220] In RAM 503, various programs and data required for the operation of system 500 are stored. Processor 501, ROM 502 and RAM 503 are connected to each other through bus 504. Processor 501 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 502 and / or RAM 503. It should be noted that the program can also be stored in one or more memories other than ROM 502 and RAM 503. Processor 501 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0221] According to an embodiment of the present disclosure, the system 500 may further include an input / output (I / O) interface 505, which is also connected to the bus 504. The system 500 may further include one or more of the following components connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, a modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage section 508 as needed.

[0222] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0223] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0224] According to an embodiment of the present disclosure, a computer-readable storage medium may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, optical cable, radio frequency signal, etc., or any suitable combination of the above.

[0225] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 502 and / or the RAM 503 described above and / or one or more memories other than the ROM 502 and the RAM 503 .

[0226] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0227] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0228] The embodiments of the present disclosure are described above. However, these embodiments are only for the purpose of illustration and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. An information processing method, comprising: Obtaining order information of a target order, wherein the order information includes user information and product information; Determine, based on the order information, a plurality of candidate coupons that can be used to reduce or exempt a payment amount when settling the target order, wherein the candidate coupons are obtained by querying a coupon server in a coupon management server cluster; Determine a mutually exclusive relationship between any two of the plurality of candidate coupons, wherein the coupons with the mutually exclusive relationship cannot be used at the same time; and At least according to the mutually exclusive relationship, and based on the plurality of candidate coupons, output a target coupon combination whose discount amount meets a preset condition for use in settling the target order; The step of outputting a target coupon combination whose discount amount meets a preset condition based on at least the mutually exclusive relationship and the plurality of candidate coupons includes: Construct an objective function for calculating the discount amount; Setting an initial solution of the objective function, wherein the initial solution is an empty set; When the iteration condition is met, the first loop body set in advance is executed. Wherein, the first loop body includes: According to the mutually exclusive relationship, a candidate coupon is selected from the multiple candidate coupons and incorporated into the initial coupon combination corresponding to the initial solution to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by the intermediate solution and the coupons contained therein are mutually exclusive with each other, and the mutually exclusive relationship is determined by constructing a coupon mutual exclusion matrix based on the application conditions of the candidate coupons to ensure that the intermediate coupon combination meets the mutually exclusive constraint conditions; Input the intermediate solution into the objective function and output the corresponding objective function value, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order; Using the intermediate solution as a new initial solution; and Using the intermediate coupon combination as a new initial coupon combination; After the iteration is finished, extracting an objective function value that satisfies a preset value from all objective function values ​​obtained by executing the first loop body; and The intermediate coupon combination corresponding to the objective function value is used as the target coupon combination.

2. The method according to claim 1, wherein: The step of outputting a target coupon combination whose discount amount meets a preset condition based on at least the mutually exclusive relationship and the plurality of candidate coupons includes: Construct an objective function for calculating the discount amount; Setting an initial solution of the objective function, wherein the initial solution is an empty set; When the iteration condition is met, the pre-set second loop body is executed. Wherein, the second loop body comprises: randomly selecting a candidate coupon from the plurality of candidate coupons; Determine whether the candidate coupons and each coupon in the initial coupon combination corresponding to the initial solution satisfy the mutually exclusive relationship; If satisfied, the candidate coupon is merged into the initial coupon combination to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by an intermediate solution; Input the intermediate solution into the objective function and output the corresponding objective function value, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order; Using the intermediate solution as a new initial solution; and Using the intermediate coupon combination as a new initial coupon combination; After the iteration is finished, extracting an objective function value that satisfies a preset value from all objective function values ​​obtained by executing the second loop body; and The intermediate coupon combination corresponding to the objective function value is used as the target coupon combination.

3. The method according to claim 2, wherein: In the second loop: If the candidate coupon and each coupon in the initial coupon combination corresponding to the initial solution do not satisfy the mutually exclusive relationship, the candidate coupon is incorporated into the initial coupon combination and the coupons in the initial coupon combination that are mutually exclusive with the candidate coupon are deleted to obtain a corresponding intermediate coupon combination.

4. The method according to claim 1, wherein: The method further comprises: Determine the contribution value of each coupon among the plurality of candidate coupons to the discount amount when used alone to settle the target order; The step of outputting a target coupon combination whose discount amount meets a preset condition based on at least the mutually exclusive relationship and the plurality of candidate coupons includes: According to the mutually exclusive relationship and the size of the contribution value, and based on the plurality of candidate coupons, a target coupon combination whose discount amount meets the preset condition is output.

5. The method according to claim 1, wherein: Determining, based on the order information, a plurality of candidate coupons that can be used to reduce or exempt the payment amount when settling the target order includes: Extracting user information and object information from the order information, wherein the user information corresponds to a target user associated with the target order, and the object information corresponds to a transaction object included in the target order; Determining all coupons available to the target user based on the user information; and According to the object information, coupons that can be used by the transaction object are selected from all the coupons to obtain the multiple candidate coupons.

6. The method according to claim 1, wherein: The determining of the mutually exclusive relationship between any two of the plurality of candidate coupons includes: Obtaining usage rules for each of the plurality of candidate coupons; and The mutually exclusive relationship between any two of the plurality of candidate coupons is determined according to the usage rule.

7. An information processing system comprising: An acquisition module, used to acquire order information of a target order, wherein the order information includes user information and product information; A first determination module is used to determine, based on the order information, a plurality of candidate coupons that can be used to reduce or exempt the payment amount when settling the target order, wherein the candidate coupons are obtained by querying a coupon server in a coupon management server cluster; A second determination module is configured to determine mutually exclusive relationships between any two of the plurality of candidate coupons, wherein the coupons with mutually exclusive relationships cannot be used at the same time; and an output module, configured to output a target coupon combination whose discount amount meets a preset condition based on at least the mutually exclusive relationship and the plurality of candidate coupons, for use in settling the target order; Wherein, the output module includes: A first construction unit is used to construct an objective function for calculating the preferential amount; A first setting unit, used to set an initial solution of the objective function, wherein the initial solution is an empty set; The first processing unit is used to execute a preset first loop body when an iteration condition is met. Wherein, the first loop body includes: According to the mutually exclusive relationship, a candidate coupon is selected from the multiple candidate coupons and incorporated into the initial coupon combination corresponding to the initial solution to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by the intermediate solution and the coupons contained therein are mutually exclusive with each other, and the mutually exclusive relationship is determined by constructing a coupon mutual exclusion matrix based on the application conditions of the candidate coupons to ensure that the intermediate coupon combination meets the mutually exclusive constraint conditions; Input the intermediate solution into the objective function and output the corresponding objective function value, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order; Using the intermediate solution as a new initial solution; and Using the intermediate coupon combination as a new initial coupon combination; A first extraction unit is used to extract an objective function value that satisfies a preset value from all objective function values ​​obtained by executing the first loop body after the iteration ends; and The first determining unit is configured to use the intermediate coupon combination corresponding to the objective function value as the target coupon combination.

8. The system according to claim 7, wherein: The output module comprises: The second construction unit is used to construct an objective function for calculating the preferential amount; A second setting unit, used to set an initial solution of the objective function, wherein the initial solution is an empty set; The second processing unit is used to execute a preset second loop body when the iteration condition is met. Wherein, the second loop body comprises: randomly selecting a candidate coupon from the plurality of candidate coupons; Determine whether the candidate coupons and each coupon in the initial coupon combination corresponding to the initial solution satisfy the mutually exclusive relationship; If satisfied, the candidate coupon is merged into the initial coupon combination to obtain an intermediate coupon combination, wherein the intermediate coupon combination is represented by an intermediate solution; Input the intermediate solution into the objective function and output the corresponding objective function value, wherein the objective function value represents the amount of discount that can be obtained by using the intermediate coupon combination to settle the target order; Using the intermediate solution as a new initial solution; and Using the intermediate coupon combination as a new initial coupon combination; A second extraction unit is used to extract an objective function value that satisfies a preset value from all objective function values ​​obtained by executing the second loop body after the iteration ends; and The second determining unit is configured to use the intermediate coupon combination corresponding to the objective function value as the target coupon combination.

9. The system according to claim 8, wherein: In the second loop: If the candidate coupon and each coupon in the initial coupon combination corresponding to the initial solution do not satisfy the mutually exclusive relationship, the candidate coupon is incorporated into the initial coupon combination and the coupons in the initial coupon combination that are mutually exclusive with the candidate coupon are deleted to obtain a corresponding intermediate coupon combination.

10. The system according to claim 7, wherein: The system further comprises: A third determination module is used to determine the contribution value of each coupon in the plurality of candidate coupons to the discount amount when each coupon is used to settle the target order alone; The output module is also used for: According to the mutually exclusive relationship and the size of the contribution value, and based on the plurality of candidate coupons, a target coupon combination whose discount amount meets the preset condition is output.

11. The system according to claim 7, wherein: The first determining module comprises: a third extraction unit, configured to extract user information and object information from the order information, wherein the user information corresponds to a target user associated with the target order, and the object information corresponds to a transaction object included in the target order; A third determining unit is used to determine all coupons that the target user can use based on the user information; and The selection unit is used to select coupons that can be used by the transaction object from all the coupons according to the object information to obtain the multiple candidate coupons.

12. The system according to claim 7, wherein: The second determining module comprises: an acquisition unit, configured to acquire a usage rule of each coupon among the plurality of candidate coupons; and The fourth determining unit is configured to determine the mutually exclusive relationship between any two of the plurality of candidate coupons according to the usage rule.

13. A computer system comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

14. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 6.

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