Credit limit determination method of game account and related equipment
By obtaining the credit limit and order data of the virtual game account, and using preset algorithms to divide and evaluate the credit limit, the problem of low efficiency in credit limit determination in virtual games is solved, and efficient batch processing of credit limits is achieved.
Patent Information
- Application Number
- CN202411149415.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-21
- Publication Date
- 2024-12-31
AI Technical Summary
The prior art has low efficiency in determining the credit limit of game accounts in virtual games, especially in large virtual games, which requires complex analysis of each player's past consumption records, resulting in reduced efficiency.
By obtaining the credit limit and order data of the game account in the specified historical cycle, using a preset algorithm to divide the game account into different credit limits to be evaluated, batch evaluation of the target credit limit, and improving the efficiency of the credit limit determination.
It realizes the unified batch processing of credit limits for game accounts, improves the efficiency of credit limit determination, and does not require complex analysis for each game account.
Smart Images
Figure CN119228524A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of computer processing technology, and in particular to a method for determining a credit limit of a game account and related equipment. Background Art
[0002] In virtual games, a certain amount of credit is usually set for the player's game account to prepay for the player's consumption in the virtual game to improve the player's payment experience. The credit limit is not fixed. In different payment cycles, the credit limit can be reduced, increased, or unchanged. When recalculating the player's credit limit, the existing technology usually analyzes each player's past consumption record to determine the player's credit limit. The larger the virtual game's player volume, the lower the efficiency of credit limit determination. Summary of the invention
[0003] In view of this, the purpose of the present disclosure is to provide a method for determining the credit limit of a game account and related devices to improve the efficiency of determining the credit limit of a game account.
[0004] In a first aspect, an embodiment of the present disclosure provides a method for determining a credit limit of a game account, the method comprising: obtaining first credit limits and order data of multiple first game accounts in a specified historical period; wherein the first credit limit corresponds to a credit limit to be evaluated; determining multiple target game accounts corresponding to the credit limit to be evaluated based on the order data; evaluating the credit limits corresponding to the multiple target game accounts through a preset algorithm corresponding to the credit limit to be evaluated, and obtaining the target credit limits of the target game accounts in the current period.
[0005] In a second aspect, an embodiment of the present disclosure provides a device for determining a credit limit of a game account, the device comprising: an acquisition module, used to obtain first credit limits and order data of multiple first game accounts in a specified historical period; wherein the first credit limit corresponds to a credit limit to be evaluated; a determination module, used to determine, based on the order data, multiple target game accounts corresponding to the credit limit to be evaluated; an evaluation module, used to evaluate the credit limits corresponding to the multiple target game accounts through a preset algorithm corresponding to the credit limit to be evaluated, to obtain the target credit limits of the target game accounts in the current period.
[0006] In a third aspect, an embodiment of the present disclosure provides an electronic device, including a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the above-mentioned method for determining the credit limit of a game account.
[0007] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned method for determining the credit limit of a game account.
[0008] The embodiments of the present disclosure bring the following beneficial effects:
[0009] The above-mentioned method for determining the credit limit of the game account and the related equipment divide the game accounts into corresponding credit limits to be evaluated in batches according to the credit limits and order data of the game accounts in a specified historical period, and then use the credit limit evaluation algorithms corresponding to different credit limits to batch evaluate the target credit limits of the game accounts under different credit limits to be evaluated, so that the credit limits of the game accounts can be processed uniformly in batches without the need for complex analysis of each game account, thereby improving efficiency.
[0010] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or understood by practicing the present disclosure. The purpose and other advantages of the present disclosure are realized and obtained by the structures particularly pointed out in the description, claims and drawings.
[0011] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0012] In order to more clearly illustrate the specific embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the specific embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.
[0013] Figure 1 A flow chart of an embodiment of a method for determining a credit limit of a game account in an embodiment of the present disclosure;
[0014] Figure 2 A schematic diagram of a device for determining a credit limit of a game account provided by an embodiment of the present disclosure;
[0015] Figure 3 A schematic diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0016] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present disclosure.
[0017] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0018] For ease of understanding, the specific process of the embodiment of the present disclosure is described below. Figure 1 In one embodiment of the present disclosure, a method for determining the credit limit of a game account includes:
[0019] Step S10, obtaining first credit limits and order data of multiple first game accounts in a specified historical period; wherein the first credit limit corresponds to the credit limit to be evaluated;
[0020] It should be noted that the credit limit refers to the amount of real / virtual currency that can be overdrawn in the game account of the player, which can be used to obtain equivalent game resources. Players can overdraw game resources through the credit limit of the game account, and can obtain game resources without paying real / virtual currency within the cycle, making it more convenient for players to obtain game resources. It is understandable that the credit limit is cyclical. Before using the credit limit in a cycle, the credit limit overdrawn in the previous cycle must be repaid first. The setting of the cycle length is not limited here.
[0021] In this embodiment, the first game account can be all game accounts in the target game, or it can be a specified part of the game accounts in the target game. For example, the first game account can be a game account normally used in the target game, a game account with order data in a specified historical period, or a game account with adjustable space in the first credit limit in a specified historical period, etc., which are specified in the target game, and the specific details are not limited here.
[0022] It is understandable that the credit limit of a game account in different periods can be evaluated according to a preset evaluation frequency, wherein the preset evaluation frequency can be a specified number of periods, for example, an evaluation is performed every 1 period, and the preset evaluation frequency can also be other specified durations, which are not specifically limited here. In this embodiment, each first game account has a first credit limit corresponding to a specified historical period, and the specified historical period can be the period when the credit limit was last evaluated, which can be specifically determined according to the above-mentioned preset evaluation frequency, which is not limited here.
[0023] In this implementation, the order data corresponding to each first game account is used to indicate the orders of the first game account in a specified historical period. The order quantity can be 0 or greater than 0, that is, the first game account can have no orders or multiple orders in the specified historical period, which is not limited here.
[0024] It should be noted that each first credit line corresponds to a credit line to be evaluated. The credit line to be evaluated corresponding to each first credit line can be preset or obtained by calculation. Specifically, it can be calculated based on the order data of the first game account in a specified historical period, or it can be calculated based on other game data of the first game account, such as game behavior data, etc., which is not limited here.
[0025] Step S20: Determine multiple target game accounts corresponding to the credit limits to be evaluated based on the order data;
[0026] In this implementation, the credit limit to be evaluated corresponding to each first game account can be determined through order data, thereby obtaining multiple target game accounts corresponding to each credit limit to be evaluated, wherein each target game account corresponds to a first game account, and the number of target game accounts is less than or equal to the number of first game accounts. In this implementation, different game accounts can be simply divided through order data, and a unified algorithm is subsequently used to calculate the credit limit of the game account corresponding to the same credit limit to be evaluated, without having to analyze the credit limit of each game account based on its order data, thereby improving the efficiency of credit limit determination.
[0027] In one embodiment, the first game accounts are divided into corresponding credit limits to be evaluated based on the order data of each first game account in a specified historical period, so as to determine multiple target game accounts corresponding to each credit limit to be evaluated, so as to uniformly and batch-wise perform credit limit evaluation on multiple target game accounts corresponding to each credit limit to be evaluated, thereby improving the efficiency of credit limit determination.
[0028] Step S30: Evaluate the credit limits of multiple target game accounts using a preset algorithm corresponding to the credit limits to be evaluated, and obtain the target credit limits of the target game accounts in the current cycle.
[0029] In this embodiment, the credit limits of multiple target game accounts corresponding to each credit limit to be evaluated can be evaluated by using preset algorithms corresponding to different credit limits to be evaluated, thereby obtaining the target credit limit of the target game account in the current cycle. The evaluation result of the preset algorithm can be used to indicate whether the credit limit of the corresponding target game account is increased / decreased to the corresponding credit limit to be evaluated, thereby determining whether the target credit limit of the target game account in the current cycle is to maintain the original credit limit (i.e., the first credit limit) or to increase / decrease to the corresponding credit limit to be evaluated.
[0030] It should be noted that the preset algorithms corresponding to different credit limits to be evaluated can be freely set according to different games, and are not specifically limited here. In one embodiment, the preset algorithm may include a calculation method and a judgment threshold of the evaluation value, wherein the evaluation value is used to indicate the recommended degree of adjusting the credit limit to the corresponding credit limit to be evaluated. Different credit limits to be evaluated may have different calculation methods for the evaluation values, or may have the same calculation method for the evaluation values and different judgment thresholds. For example, the calculation method for the evaluation values of credit limit 6 to be evaluated and credit limit 8 to be evaluated may be the same, but the judgment thresholds of the evaluation values may be different. For example, the judgment threshold of the evaluation value of credit limit 6 to be evaluated may be 20%, and the judgment threshold of the evaluation value of credit limit 6 to be evaluated may be 30%. That is, when the evaluation value is greater than 20%, the credit limit can be adjusted to 6, and when the evaluation value is greater than 30%, the credit limit can be adjusted to 8. No specific limitation is made here.
[0031] The method for determining the credit limit of a game account provided in the above-mentioned embodiment divides the game accounts into corresponding credit limits to be evaluated in batches according to the credit limits and order data of the game accounts in a specified historical period, and then uses the credit limit evaluation algorithms corresponding to different credit limits to batch evaluate the target credit limits of the game accounts under different credit limits to be evaluated, so that the credit limits of the game accounts can be processed uniformly in batches without the need for complex analysis of each game account, thereby improving efficiency.
[0032] Next, a method of dividing the first game account into different credit limits to be evaluated is specifically described.
[0033] In one embodiment, when determining multiple target game accounts corresponding to the credit limit to be evaluated based on order data, the payment success rate of each first game account for orders of specified amounts is calculated based on the order data; wherein, orders of specified amounts refer to orders with amounts greater than or equal to the corresponding credit limit to be evaluated; based on the payment success rate, multiple target game accounts corresponding to the credit limit to be evaluated are determined.
[0034] In this implementation, by simply calculating the payment success rate of the specified amount in the order of the first game account, the first game account can be uniformly divided into different credit limits to be evaluated, making the determination of the credit limit more efficient. It should be noted that each first game account corresponds to a first credit limit, and the first credit limit corresponds to a credit limit to be evaluated. In this implementation, the specified amount order refers to an order with an amount greater than or equal to the credit limit to be evaluated corresponding to the first game account. For example, assuming that the first credit limit of the first game account A is 6, and the first credit limit is 6 and the corresponding credit limit to be evaluated is 8, then the specified amount order refers to an order of the first game account A with an amount greater than or equal to 8, and the specifics are not limited here.
[0035] In one embodiment, the orders of the first game account can also be divided into multiple specified amounts based on the order data, wherein the specified amount is an amount in multiple preset credit limits that is greater than or equal to the credit limit to be evaluated corresponding to the first game account, the multiple preset credit limits constitute a credit limit system, and the first credit limit and the credit limit to be evaluated are both amounts in the credit limit system. Next, the payment success rate of each order of the specified amount of the first game account is calculated, and based on the payment success rate of each order of the specified amount, multiple target game accounts corresponding to the credit limit to be evaluated are determined.
[0036] It can be understood that the higher the payment success rate, the higher the payment ability of the first game account for the order of the specified amount. Therefore, according to the payment success rate corresponding to each first game account, it can be determined to allocate the first game account to the corresponding credit limit to be evaluated, thereby obtaining multiple target game accounts corresponding to each credit limit to be evaluated.
[0037] In one embodiment, when calculating the payment success rate of each first game account for orders of a specified amount based on order data, target orders of a specified amount are screened out from the orders corresponding to each first game account based on the order data; wherein the target orders include payment failed orders and / or payment successful orders; and the ratio of the number of payment successful orders of each first game account to the target orders is calculated to obtain the payment success rate of each first game account for orders of a specified amount.
[0038] In this implementation, the payment success rate is the ratio of successfully paid orders of a specified amount in the order data of the first game account to the total number of orders. For example, assuming that there are 4 successfully paid 6-yuan orders and 6 failed 6-yuan orders in the order data of the first game account, then the payment success rate of the 6-yuan orders of the first game account is 4 / (4+6)=40%, which is not limited here.
[0039] In one embodiment, when determining multiple target game accounts corresponding to the credit limit to be evaluated based on the payment success rate, the first game account whose payment success rate is greater than or equal to a preset threshold is determined as the target game account corresponding to the credit limit to be evaluated, and multiple target game accounts corresponding to the credit limit to be evaluated are obtained.
[0040] It is understandable that when the first game accounts are divided into different credit lines to be evaluated, the first game accounts with a payment success rate greater than or equal to a preset threshold value can be divided into the corresponding credit lines to be evaluated, so as to obtain multiple target game accounts corresponding to each credit line to be evaluated, wherein each first credit line or credit line to be evaluated can correspond to a preset threshold value. For example, assuming that the credit line to be evaluated corresponding to the first credit line 6 is 8, and the preset threshold value corresponding to the credit line to be evaluated is 80%, then, among all the first game accounts corresponding to the first credit line 6, the first game accounts with a payment success rate greater than or equal to 80% can be divided into the credit line 8 to be evaluated, as the target game account corresponding to the credit line 8 to be evaluated, for batch unified credit line evaluation.
[0041] The following is a detailed description of the method for batch evaluation of the credit limit of the target game account.
[0042] In one embodiment, the credit limits corresponding to multiple target game accounts are evaluated by a preset algorithm corresponding to the credit limits to be evaluated. When the target credit limits of the target game accounts in the current period are obtained, the game behavior data of each target game account in a specified historical period is obtained; based on the game behavior data and the order data of the target game account in the specified historical period, the target confidence level of each target game account to be increased to the corresponding credit limit to be evaluated is calculated; based on the target confidence level, the target credit limit of each target game account in the current period is determined.
[0043] In this embodiment, based on the game behavior data and order data of the target game account in a specified historical period, the target confidence level of the credit limit of the target game account can be calculated to be adjusted to the target credit limit to be evaluated, and the target credit limit of the target game account in the current period can be determined based on the target confidence level. Among them, the game behavior data can be relevant data of any game behavior of the target game account, such as game login data, game task data, game resource disposal data, etc., which are not limited here.
[0044] In this embodiment, according to the game behavior data and order data, the target confidence of each target game account adjusted to the corresponding credit limit to be evaluated can be calculated according to different confidence calculation algorithms set for different games, wherein the target confidence is used to indicate the recommended degree of adjusting the credit limit of the target game account to the corresponding credit limit to be evaluated. According to the target confidence, it can be determined whether to adjust the credit limit of the target game account to the corresponding credit limit to be evaluated, thereby obtaining the target credit limit of the target game account in the current cycle. It can be understood that the target credit limit of the target game account in the current cycle can be the first credit limit of the target game account, or it can be the credit limit to be evaluated corresponding to the target game account. Through different confidence calculation algorithms, the credit limit of the game account can be uniformly adjusted to the specified credit limit, or the original credit limit can be maintained, without using a complex credit limit calculation algorithm to calculate the credit limit for each game account, so that the efficiency of credit limit determination is improved, which can be used for batch game account processing.
[0045] In one embodiment, when calculating the target confidence level for adjusting each target game account to a corresponding credit limit to be evaluated based on the game behavior data and the order data of the target game account in a specified historical period, the game login frequency of each target game account in the specified historical period is calculated based on the game behavior data; the payment frequency of orders of a specified amount for each target game account and the proportion of payment amounts of orders of a specified amount are calculated based on the order data; and the target confidence level for adjusting each target game account to a corresponding credit limit to be evaluated is calculated based on the game login frequency, payment frequency, and payment amount proportion of each target game account.
[0046] Specifically, the game login frequency of the target game account can be calculated based on the game behavior data of the target game account, and then the payment frequency of the target game account's specified amount orders and the payment amount ratio of the specified amount orders can be calculated based on the order data. The target confidence can be calculated based on the game login frequency, payment frequency, and payment amount ratio. For example, assuming that the number of game days of the target game account A in the specified historical period of 30 days is 15 days, then the game login frequency of the target game account A is 15 / 30=0.5, assuming that the specified amount is 6, and the number of payment times of the target game account A's order of 6 in 30 days is 3, then the payment frequency is 3 / 30=0.1, assuming that the total payment amount of the target game account A in 30 days is 180, then the payment amount ratio is 6×3 / 180=0.1, then, according to 0.5, 0.1, 0.1, the target confidence of the target game account A can be calculated.
[0047] In one embodiment, when calculating the target confidence level for adjusting each target game account to the corresponding credit limit to be evaluated based on the game login frequency, payment frequency, and payment amount ratio of each target game account, the game login frequency, payment frequency, and payment amount ratio of each target game account are weighted and superimposed to obtain the target confidence level for adjusting each target game account to the corresponding credit limit to be evaluated.
[0048] That is, when calculating the target confidence, the game login frequency, payment frequency, and payment amount ratio of each target game account can be weighted and superimposed according to the weight values corresponding to the game login frequency, payment frequency, and payment amount ratio, so as to obtain the target confidence. For example, assuming that the weight values corresponding to the game login frequency, payment frequency, and payment amount ratio are 0.3, 0.3, and 0.4, respectively, then based on the game login frequency of 0.5, payment frequency of 0.1, and payment amount ratio of 0.1 calculated in the previous example, the calculated target confidence is: 0.3×0.5+0.3×0.1+0.4×0.1=0.22, which is not limited here.
[0049] In one embodiment, when determining the target credit limit of each target game account in the current cycle based on the target confidence, the specified confidence corresponding to the credit limit to be evaluated is obtained; the target credit limit of the target game account in the current cycle whose target confidence is greater than the corresponding specified confidence is determined as the corresponding credit limit to be evaluated, and the target credit limit of the target game account in the current cycle whose target confidence is less than or equal to the corresponding specified confidence is determined as the corresponding first credit limit.
[0050] In this embodiment, each credit limit to be evaluated corresponds to a specified confidence level. In the above embodiment, the target confidence level of the target game account adjusted to the corresponding credit limit to be evaluated is obtained, the target confidence level of each target game account is compared with the specified confidence level of the credit limit to be evaluated corresponding to the target game account, and the credit limit of the target game account with a larger target confidence level is adjusted to the corresponding credit limit to be evaluated, that is, the credit limit evaluation result is to readjust the credit limit, and the credit limit of the target game account with a smaller or equal target confidence level is adjusted to the corresponding first credit limit, that is, the credit limit evaluation result is to maintain the original credit limit.
[0051] Corresponding to the above method embodiment, see Figure 2A schematic diagram of a device for determining a credit limit of a game account is shown, the device comprising: an acquisition module 201, used to obtain first credit limits and order data of multiple first game accounts in a specified historical period; wherein the first credit limit corresponds to a credit limit to be evaluated; a determination module 202, used to determine multiple target game accounts corresponding to the credit limit to be evaluated based on the order data; an evaluation module 203, used to evaluate the credit limits corresponding to the multiple target game accounts through a preset algorithm corresponding to the credit limit to be evaluated, and obtain the target credit limits of the target game accounts in the current period.
[0052] The above-mentioned device for determining the credit limit of the game account divides the game accounts into corresponding credit limits to be evaluated in batches according to the credit limits and order data of the game accounts in a specified historical period, and then uses the credit limit evaluation algorithms corresponding to different credit limits to batch evaluate the target credit limits of the game accounts under different credit limits to be evaluated, so that the credit limits of the game accounts can be processed uniformly in batches without the need for complex analysis of each game account, thereby improving efficiency.
[0053] Optionally, the above-mentioned determination module 202 includes: a first calculation unit, used to calculate the payment success rate of each first game account for the specified amount order according to the order data; wherein the specified amount order refers to an order with an amount greater than or equal to the corresponding credit limit to be evaluated; the first determination unit is used to determine multiple target game accounts corresponding to the credit limit to be evaluated according to the payment success rate.
[0054] Optionally, the first calculation unit is specifically used to: filter out target orders of a specified amount from the orders corresponding to each first game account according to the order data; wherein the target orders include payment failed orders and / or payment successful orders; calculate the ratio of the number of payment successful orders of each first game account to the target orders, and obtain the payment success rate of each first game account for orders of the specified amount.
[0055] Optionally, the first determining unit is specifically configured to: determine the first game account whose payment success rate is greater than or equal to a preset threshold as a target game account corresponding to the credit limit to be evaluated, and obtain multiple target game accounts corresponding to the credit limit to be evaluated.
[0056] Optionally, the evaluation module 203 includes: an acquisition submodule, used to acquire game behavior data of each target game account in a specified historical period; a calculation submodule, used to calculate the target confidence of each target game account adjusted to the corresponding credit limit to be evaluated based on the game behavior data and the order data of the target game account in the specified historical period; and a determination submodule, used to determine the target credit limit of each target game account in the current period based on the target confidence.
[0057] Optionally, the above-mentioned calculation submodule includes: a second calculation unit, used to calculate the game login frequency of each target game account in a specified historical period according to the game behavior data; a third calculation unit, used to calculate the payment frequency of orders of a specified amount for each target game account, and the payment amount ratio of orders of a specified amount according to the order data; a fourth calculation unit, used to calculate the target confidence of each target game account adjusted to the corresponding credit limit to be evaluated according to the game login frequency, the payment frequency, and the payment amount ratio of each target game account.
[0058] Optionally, the fourth calculation unit is specifically used to: perform weighted superposition on the game login frequency, the payment frequency, and the payment amount proportion of each target game account to obtain a target confidence level of each target game account adjusted to a corresponding credit limit to be evaluated.
[0059] Optionally, the above-mentioned determination submodule is specifically used to: obtain a specified confidence corresponding to the credit limit to be evaluated; determine the target credit limit of the target game account whose target confidence is greater than the corresponding specified confidence in the current period as the corresponding credit limit to be evaluated, and determine the target credit limit of the target game account whose target confidence is less than or equal to the corresponding specified confidence in the current period as the corresponding first credit limit.
[0060] This embodiment also provides an electronic device, including a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the above-mentioned method for determining the credit limit of a game account. The electronic device can be a server or a terminal device.
[0061] See also Figure 3 As shown, the electronic device includes a processor 100 and a memory 101, wherein the memory 101 stores machine executable instructions that can be executed by the processor 100, and the processor 100 executes the machine executable instructions to implement the above-mentioned method for determining the credit limit of a game account.
[0062] Further, Figure 3 The electronic device shown further includes a bus 102 and a communication interface 103 , and the processor 100 , the communication interface 103 and the memory 101 are connected via the bus 102 .
[0063] The memory 101 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 103 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 102 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.
[0064] The processor 100 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 100. The above processor 100 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 101, and the processor 100 reads the information in the memory 101 and completes the steps of the method of the above embodiment in combination with its hardware, for example:
[0065] Acquire first credit limits and order data of multiple first game accounts in a specified historical period; wherein the first credit limit corresponds to a credit limit to be evaluated; determine multiple target game accounts corresponding to the credit limit to be evaluated based on the order data; evaluate the credit limits corresponding to the multiple target game accounts through a preset algorithm corresponding to the credit limit to be evaluated, and obtain the target credit limit of the target game account in the current period.
[0066] In this method, game accounts are uniformly divided into corresponding credit limits to be evaluated in batches according to their credit limits and order data in a specified historical period, and then the credit limit evaluation algorithms corresponding to different credit limits are used to batch evaluate the target credit limits of game accounts under different credit limits to be evaluated. This allows the credit limits of game accounts to be processed uniformly in batches without the need for complex analysis of each game account, thereby improving efficiency.
[0067] Optionally, the step of determining multiple target game accounts corresponding to the credit limit to be evaluated based on the order data includes: calculating the payment success rate of each first game account for orders of a specified amount based on the order data; wherein the order of a specified amount refers to an order with an amount greater than or equal to the corresponding credit limit to be evaluated; and determining the multiple target game accounts corresponding to the credit limit to be evaluated based on the payment success rate.
[0068] Optionally, the step of calculating the payment success rate of each first game account for orders of a specified amount based on the order data includes: filtering out target orders of a specified amount from the orders corresponding to each first game account based on the order data; wherein the target orders include payment failed orders and / or payment successful orders; calculating the ratio of the number of payment successful orders of each first game account to the target orders, and obtaining the payment success rate of each first game account for orders of the specified amount.
[0069] Optionally, the step of determining multiple target game accounts corresponding to the credit limit to be evaluated based on the payment success rate includes: determining a first game account whose payment success rate is greater than or equal to a preset threshold as the target game account corresponding to the credit limit to be evaluated, and obtaining multiple target game accounts corresponding to the credit limit to be evaluated.
[0070] Optionally, the step of evaluating the credit limits of multiple target game accounts corresponding to the credit limits to be evaluated by using a preset algorithm corresponding to the credit limits to be evaluated to obtain the target credit limits of the target game accounts in the current period includes: obtaining the game behavior data of each target game account in a specified historical period; calculating the target confidence of each target game account adjusted to the corresponding credit limit to be evaluated based on the game behavior data and the order data of the target game account in the specified historical period; and determining the target credit limit of each target game account in the current period based on the target confidence.
[0071] Optionally, the step of calculating a target confidence level for each target game account to be adjusted to a corresponding credit limit to be evaluated based on the game behavior data and the order data of the target game account in a specified historical period includes: calculating a game login frequency for each target game account in a specified historical period based on the game behavior data; calculating a payment frequency for orders of a specified amount and a payment amount ratio of orders of a specified amount for each target game account based on the order data; and calculating a target confidence level for each target game account to be adjusted to a corresponding credit limit to be evaluated based on the game login frequency, payment frequency, and payment amount ratio of each target game account.
[0072] Optionally, the step of calculating a target confidence level for adjusting each target game account to a corresponding credit limit to be evaluated based on the game login frequency, payment frequency, and payment amount ratio of each target game account includes: weighted superposition of the game login frequency, payment frequency, and payment amount ratio of each target game account to obtain a target confidence level for adjusting each target game account to a corresponding credit limit to be evaluated.
[0073] Optionally, the step of determining the target credit limit of each target game account in the current cycle according to the target confidence includes: obtaining a specified confidence corresponding to the credit limit to be evaluated; determining the target credit limit of the target game account in the current cycle whose target confidence is greater than the corresponding specified confidence as the corresponding credit limit to be evaluated, and determining the target credit limit of the target game account in the current cycle whose target confidence is less than or equal to the corresponding specified confidence as the corresponding first credit limit.
[0074] This embodiment also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned method for determining the credit limit of a game account, for example:
[0075] Acquire first credit limits and order data of multiple first game accounts in a specified historical period; wherein the first credit limit corresponds to a credit limit to be evaluated; determine multiple target game accounts corresponding to the credit limit to be evaluated based on the order data; evaluate the credit limits corresponding to the multiple target game accounts through a preset algorithm corresponding to the credit limit to be evaluated, and obtain the target credit limit of the target game account in the current period.
[0076] In this method, game accounts are uniformly divided into corresponding credit limits to be evaluated in batches according to their credit limits and order data in a specified historical period, and then the credit limit evaluation algorithms corresponding to different credit limits are used to batch evaluate the target credit limits of game accounts under different credit limits to be evaluated. This allows the credit limits of game accounts to be processed uniformly in batches without the need for complex analysis of each game account, thereby improving efficiency.
[0077] Optionally, the step of determining multiple target game accounts corresponding to the credit limit to be evaluated based on the order data includes: calculating the payment success rate of each first game account for orders of a specified amount based on the order data; wherein the order of a specified amount refers to an order with an amount greater than or equal to the corresponding credit limit to be evaluated; and determining the multiple target game accounts corresponding to the credit limit to be evaluated based on the payment success rate.
[0078] Optionally, the step of calculating the payment success rate of each first game account for orders of a specified amount based on the order data includes: filtering out target orders of a specified amount from the orders corresponding to each first game account based on the order data; wherein the target orders include payment failed orders and / or payment successful orders; calculating the ratio of the number of payment successful orders of each first game account to the target orders, and obtaining the payment success rate of each first game account for orders of the specified amount.
[0079] Optionally, the step of determining multiple target game accounts corresponding to the credit limit to be evaluated based on the payment success rate includes: determining a first game account whose payment success rate is greater than or equal to a preset threshold as the target game account corresponding to the credit limit to be evaluated, and obtaining multiple target game accounts corresponding to the credit limit to be evaluated.
[0080] Optionally, the step of evaluating the credit limits of multiple target game accounts corresponding to the credit limits to be evaluated by using a preset algorithm corresponding to the credit limits to be evaluated to obtain the target credit limits of the target game accounts in the current period includes: obtaining the game behavior data of each target game account in a specified historical period; calculating the target confidence of each target game account adjusted to the corresponding credit limit to be evaluated based on the game behavior data and the order data of the target game account in the specified historical period; and determining the target credit limit of each target game account in the current period based on the target confidence.
[0081] Optionally, the step of calculating a target confidence level for each target game account to be adjusted to a corresponding credit limit to be evaluated based on the game behavior data and the order data of the target game account in a specified historical period includes: calculating a game login frequency for each target game account in a specified historical period based on the game behavior data; calculating a payment frequency for orders of a specified amount and a payment amount ratio of orders of a specified amount for each target game account based on the order data; and calculating a target confidence level for each target game account to be adjusted to a corresponding credit limit to be evaluated based on the game login frequency, payment frequency, and payment amount ratio of each target game account.
[0082] Optionally, the step of calculating a target confidence level for adjusting each target game account to a corresponding credit limit to be evaluated based on the game login frequency, payment frequency, and payment amount ratio of each target game account includes: weighted superposition of the game login frequency, payment frequency, and payment amount ratio of each target game account to obtain a target confidence level for adjusting each target game account to a corresponding credit limit to be evaluated.
[0083] Optionally, the step of determining the target credit limit of each target game account in the current cycle according to the target confidence includes: obtaining a specified confidence corresponding to the credit limit to be evaluated; determining the target credit limit of the target game account in the current cycle whose target confidence is greater than the corresponding specified confidence as the corresponding credit limit to be evaluated, and determining the target credit limit of the target game account in the current cycle whose target confidence is less than or equal to the corresponding specified confidence as the corresponding first credit limit.
[0084] The computer program product of the method, device, electronic device and storage medium for determining the credit limit of a game account provided in the embodiments of the present disclosure includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be found in the method embodiments, which will not be repeated here.
[0085] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.
[0086] In addition, in the description of the embodiments of the present disclosure, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present disclosure can be understood according to specific circumstances.
[0087] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.
[0088] In the description of the present disclosure, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present disclosure and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present disclosure. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.
[0089] Finally, it should be noted that the above embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the above embodiments, those skilled in the art should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.
Claims
1. A method for determining the credit limit of a game account, characterized in that: The method comprises: Obtaining first credit limits and order data of a plurality of first game accounts in a specified historical period; wherein the first credit limits correspond to credit limits to be evaluated; Determining, based on the order data, a plurality of target game accounts corresponding to the credit limit to be evaluated; The credit limits of the corresponding multiple target game accounts are evaluated by using a preset algorithm corresponding to the credit limits to be evaluated, so as to obtain the target credit limits of the target game accounts in the current cycle.
2. The method according to claim 1, characterized in that The step of determining, based on the order data, a plurality of target game accounts corresponding to the credit limits to be evaluated comprises: Calculate the payment success rate of each first game account for the specified amount order according to the order data; wherein the specified amount order refers to the order with an amount greater than or equal to the corresponding credit limit to be evaluated; According to the payment success rate, a plurality of target game accounts corresponding to the credit limit to be evaluated are determined.
3. The method according to claim 2, characterized in that The step of calculating the payment success rate of each first game account for the order of the specified amount according to the order data includes: According to the order data, filter out target orders of a specified amount from the orders corresponding to each first game account; wherein the target orders include payment failure orders and / or payment success orders; The ratio of the number of successful payment orders of each first game account to the number of target orders is calculated to obtain the payment success rate of each first game account for orders of a specified amount.
4. The method according to claim 2, characterized in that: The step of determining a plurality of target game accounts corresponding to the credit limit to be evaluated according to the payment success rate includes: The first game account whose payment success rate is greater than or equal to a preset threshold is determined as a target game account corresponding to the credit limit to be evaluated, and multiple target game accounts corresponding to the credit limit to be evaluated are obtained.
5. The method according to claim 1, characterized in that The step of evaluating the credit limits corresponding to a plurality of target game accounts by using a preset algorithm corresponding to the credit limits to be evaluated to obtain target credit limits of the target game accounts in the current cycle includes: Obtain the game behavior data of each target game account in a specified historical period; Calculate, based on the game behavior data and the order data of the target game account in a specified historical period, a target confidence level of each target game account adjusted to a corresponding credit limit to be assessed; According to the target confidence, a target credit limit for each target game account in the current cycle is determined.
6. The method according to claim 5, characterized in that The step of calculating the target confidence level of each target game account adjusted to a corresponding credit limit to be evaluated based on the game behavior data and the order data of the target game account in a specified historical period includes: Calculate the game login frequency of each target game account in a specified historical period based on the game behavior data; Calculate the payment frequency of the orders of the specified amount and the payment amount ratio of the orders of the specified amount for each target game account according to the order data; According to the game login frequency, the payment frequency, and the payment amount ratio of each target game account, a target confidence level of each target game account adjusted to a corresponding credit limit to be evaluated is calculated.
7. The method according to claim 6, characterized in that The step of calculating the target confidence level of each target game account adjusted to a corresponding credit limit to be evaluated according to the game login frequency, the payment frequency, and the payment amount ratio of each target game account includes: The game login frequency, the payment frequency, and the payment amount ratio of each target game account are weighted and superimposed to obtain a target confidence level of each target game account adjusted to a corresponding credit limit to be evaluated.
8. The method according to claim 5, characterized in that The step of determining a target credit limit for each target game account in the current cycle according to the target confidence level includes: Get the specified confidence level corresponding to the credit limit to be evaluated; The target credit limit of the target game account with a target confidence greater than the corresponding specified confidence in the current cycle is determined as the corresponding credit limit to be evaluated, and the target credit limit of the target game account with a target confidence less than or equal to the corresponding specified confidence in the current cycle is determined as the corresponding first credit limit.
9. A device for determining the credit limit of a game account, characterized in that: The device comprises: An acquisition module, used to acquire first credit limits and order data of a plurality of first game accounts in a specified historical period; wherein the first credit limit corresponds to a credit limit to be evaluated; A determination module, configured to determine, based on the order data, a plurality of target game accounts corresponding to the credit limit to be evaluated; The evaluation module is used to evaluate the credit limits of the corresponding multiple target game accounts by using a preset algorithm corresponding to the credit limits to be evaluated, so as to obtain the target credit limits of the target game accounts in the current cycle.
10. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement the credit limit determination method for a game account according to any one of claims 1 to 8.
11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the method for determining the credit limit of a game account according to any one of claims 1-8.