Data processing device and related device

Abstract

The present application relates to a data processing method and a related device. The method comprises: determining a payment mode of a user by means of a preset threshold and first information which contains three elements, wherein the three elements include: a time interval between a moment when the purchase amount of the user after registration meets a preset amount and a preset moment, a frequency at which the user purchases a first product, and a total purchase amount of the user within a preset time period. The method designs a whole set of credit risk control system for micro and small enterprises that purchase cloud services on a postpaid basis, thereby reducing the fund risk caused by short user's registration time or incomplete external information coverage.

Description

Data processing equipment and related equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on March 30, 2024, with application number 202410389443.8 and application name “A data processing device and related equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of cloud services, and in particular to a data processing method and related equipment. Background Art

[0003] With the development of cloud services, more and more companies are migrating their businesses to the cloud by purchasing cloud service resources.

[0004] Typically, these companies have small scale benefits but large numbers, and often prefer to purchase cloud service resources through a post-paid model.

[0005] Therefore, how to conduct reasonable credit assessment and risk control for these enterprises is crucial for cloud service providers. Summary of the Invention

[0006] This application provides a data processing method and related equipment that can be used to design a complete credit risk control system for small and micro businesses that purchase cloud services and receive payment after payment. This reduces the financial risks caused by short user registration time or incomplete external information coverage.

[0007] In a first aspect, a data processing method is provided. The method is performed by a data processing device, or by a component of the data processing device (e.g., a processor, chip, or chip system), or may be implemented by a logic module or software that implements all or part of the data processing device's functionality. In the first aspect and its possible implementations, the method is described using a data processing device as an example. In this method, the data processing device first obtains user information. The user information includes first information, a first amount, and a total amount spent. The first information is the time interval between the time when the user's registered consumption amount meets a preset amount and a preset time. The first amount is the frequency of the user's purchase of a first product. The total amount spent is the user's total amount spent within a preset time period. The data processing device then determines the user's payment mode based on the user information and a preset threshold. Payment modes include prepayment and postpayment. After determining the payment mode, the data processing device may present the payment mode results to the user. It is understood that the data processing device may present the payment mode results directly to the user. Alternatively, the data processing device may present the payment mode results to the user via a terminal device.

[0008] The first information can be understood as the adjusted R, which is adjusted to the time interval between the time when the user's post-registration spending meets the preset amount and the preset time. The preset time can be set based on actual needs or business experience. For example, the preset time can be the user's most recent post-registration spending, the time when the user's most recent post-registration spending exceeds the preset amount, or the time when the user sends the first request. For example, the adjusted R is the time interval between the time when the user's most recent post-registration spending exceeds the preset amount and the preset time. The adjusted R takes into account the impact of spending and cloud service billing standards. It not only filters data with amounts below the threshold but also prevents fraudulent users from purchasing cloud servers that they don't use through registration restrictions. The first quantity can be understood as the adjusted F, which is adjusted to the frequency of users purchasing the first product. The first product can be a specific cloud service product (or a key cloud service product). The adjusted F takes into account the impact of specific cloud service products on customer stickiness. The total amount of spending within the preset time period can be understood as the adjusted M, which is adjusted to the user's total spending within the preset time period. The starting time of the preset time period may be the registration time, and the ending time of the preset time period may be the time when the user sends the first request, or other time, etc., which is not limited here.

[0009] In this application, a user's payment model is determined by first information containing three elements and a preset threshold. The three elements include: the time when the user's consumption after registration meets the preset amount, the most recent consumption time between the preset time, the frequency of the user's purchase of the first product, and the user's total consumption within the preset time period. This method designs a complete credit risk control system for small and micro enterprises that purchase cloud services and pay back after payment. It reduces the financial risks caused by short user registration time or incomplete external information coverage.

[0010] In one possible implementation, the above-mentioned preset thresholds include a first threshold, a second threshold and a third threshold; determining the user's payment mode based on user information and the preset thresholds includes: determining a first value based on the first information and the first threshold; determining a second value based on the first quantity and the second threshold; determining a third value based on the total consumption and the third threshold; determining a payment mode based on the first value, the second value and the third value.

[0011] In this possible implementation, the level of each factor can be obtained by comparing the three factors with the corresponding thresholds, so that the payment mode can be determined according to the level of each factor, thereby improving the rationality of the payment mode determination.

[0012] In another possible implementation, the above step of determining the payment mode based on the first value, the second value, and the third value includes: determining the target customer group to which the user belongs based on the first value, the second value, and the third value; and determining the payment mode based on the position of the target customer group among all customer groups.

[0013] In this possible implementation, the target customer group can be determined based on the values ​​of the three factors. The payment model can then be determined based on the position or ranking of the target customer group within the overall customer group. The position of the target customer group within the overall customer group can reflect the specific high and low correspondence of the three factors. For example, a high value for all three factors corresponds to the first position, while a low value for all three factors corresponds to the last position.

[0014] In another possible implementation, the above-mentioned first value, second value and third value are 0 or 1; if the time interval is less than or equal to the first threshold, the first value of the first information is 1; if the time interval is greater than the first threshold, the first value of the first information is 0; if the first quantity is greater than or equal to the second threshold, the second value of the second information is 1; if the first quantity is less than the second threshold, the second value of the second information is 0; if the total consumption amount is greater than or equal to the third threshold, the third value of the third information is 1; if the total consumption amount is less than the third threshold, the third value of the third information is 0.

[0015] In this possible implementation, the values ​​of the three elements can be determined by introducing respective thresholds of the three elements, so that the subsequent determination of the payment mode based on the values ​​of the three elements is more reasonable.

[0016] In another possible implementation, when the values ​​of the first value, the second value and the third value are all 1, the target customer group is important value customers; when the first value is 0, the second value is 1 and the third value is 1, the target customer group is important retention customers; when the values ​​of the first value, the second value and the third value are all 0, the target customer group is general retention customers.

[0017] Several examples are provided for this possible implementation. For example, customers with recent purchases, high frequency of purchases of specific products, and high total purchase amounts are considered key value customers. Another example is customers with recent purchases, high frequency of purchases of specific products, and high total purchase amounts, are considered key retention customers. Another example is customers with recent purchases, low frequency of purchases of specific products, and low total purchase amounts, are considered general retention customers.

[0018] In another possible implementation, the above step: before obtaining user information, the method also includes: obtaining the user's public opinion information; determining the user's payment mode based on the user information and a preset threshold, including: if the public opinion information meets the preset conditions, determining the payment mode based on the user information and the preset threshold.

[0019] In this possible implementation, a precondition can be added to determine whether to proceed with the payment mode. That is, customers can be screened based on whether public opinion information meets the pre-set conditions, thereby reducing the power consumption caused by having to determine the payment mode for all customers.

[0020] In another possible implementation, the above method is applied to risk control management in a cloud enterprise scenario.

[0021] In this possible implementation, by applying the modified RFM to the credit risk control scenario, it is more suitable for determining the payment model of small and micro enterprises that purchase cloud services and pay back the money later.

[0022] In another possible implementation, the step of obtaining user information includes receiving a first request from the user, where the first request carries the user information and is used to apply for a post-payment mode.

[0023] In this possible implementation, the payment mode can be triggered and determined through the user's first request, thereby improving the user experience.

[0024] In another possible implementation, the aforementioned preset time is the time when the first request is issued.

[0025] In this possible implementation, the preset time in the three elements can be associated with the time when the first request is issued, so that the information before the time when the request is issued can be fully considered, thereby improving the accuracy of subsequent determination of the payment model.

[0026] In another possible implementation, the above step of determining the user's payment mode based on the user information and a preset threshold includes determining the payment mode and the credit amount based on the user information and the preset threshold.

[0027] In this possible implementation method, not only can the payment model be determined based on the three factors, but the credit amount can also be determined based on the level of the three factors, so that credit applications from different customers can be treated flexibly and the differences between different customers can be enhanced.

[0028] In a second aspect, a data processing device is provided, wherein the data processing device includes modules for executing the data processing method in the first aspect or any possible implementation manner of the first aspect.

[0029] In a third aspect, a data processing device is provided, comprising at least one processor, wherein the at least one processor is coupled to a memory; the memory is used to store programs or instructions; and the at least one processor is used to execute the program or instructions so that the data processing device implements a method of any possible implementation of the first aspect described above.

[0030] In a fourth aspect, a data processing device is provided, comprising at least one logic circuit and an input / output interface; the logic circuit is used to execute the method described in any possible implementation of the first aspect.

[0031] In a fifth aspect, a computer-readable storage medium is provided, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in any possible implementation of any aspect of the first aspect above.

[0032] In a sixth aspect, a computer program product is provided. When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of any aspect of the first aspect.

[0033] In a seventh aspect, a chip or chip system is provided, which includes at least one processor for supporting a communication device to implement the method described in any possible implementation method of any aspect of the first aspect.

[0034] In one possible design, the chip system may further include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of a chip or may include a chip and other discrete components. Optionally, the chip system may also include an interface circuit that provides program instructions and / or data to at least one processor.

[0035] In an eighth aspect, a data processing device is provided, comprising the chip system as described in the seventh aspect above, the chip system comprising a processor and a communication interface, the communication interface being used to communicate with modules outside the chip system shown, the processor being used to run computer programs or instructions so that the data processing device can execute the method of any of the above aspects.

[0036] In a ninth aspect, a data processing device cluster is provided, comprising at least one data processing device according to the second aspect or the eighth aspect, wherein any one of the data processing devices is configured to execute a computer program or instruction, so that the data processing device cluster can perform any of the aforementioned methods. Alternatively, some or all of the data processing devices are configured to execute the computer program or instruction, so that the data processing device cluster can perform any of the aforementioned methods.

[0037] Among them, the technical effects brought about by any design method in the second to ninth aspects can refer to the technical effects brought about by the different design methods in the above-mentioned first aspect, and will not be repeated here.

[0038] Based on the implementation methods provided in the above aspects, this application can also be further combined to provide more implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] FIG1A is a schematic diagram of the structure of the system architecture provided by this application;

[0040] FIG1B is another structural diagram of the system architecture provided by this application;

[0041] FIG2 is another structural diagram of the system architecture provided by this application;

[0042] FIG3 is a flow chart of a data processing method provided by the present application;

[0043] FIG4 is an example diagram of the relationship between the three elements provided in this application and the target customer group;

[0044] 5 to 7 are several schematic diagrams of data processing equipment involved in this application;

[0045] FIG8 is a schematic diagram of the structure of a data processing device cluster provided by the present application;

[0046] FIG9 is a schematic diagram of the structure of another data processing device cluster provided by the present application;

[0047] FIG10 is a schematic diagram of the structure of a computer-readable storage medium provided in this application. DETAILED DESCRIPTION

[0048] To facilitate understanding, the following is an introduction to the main terms and concepts involved in this application.

[0049] 1. Recency Frequency Monetary (RFM) Model

[0050] The RFM model is used to measure customer value and their ability to generate profit. R represents the time interval between the last purchase, F represents the frequency of purchase, and M represents the amount spent. For example, if a user's last purchase was on February 20th and the current date is February 25th, then R is 5 days. For another example, if a user has shopped at Supermarket A 80 times, then F is 100 times. For another example, if a user's total purchase at Supermarket A is 2,000 yuan, then M is 2,000 yuan.

[0051] On the one hand, the current RFM model is mainly used in the field of customer marketing. On the other hand, the RFM model is targeted at customers with a large number of cloud services and small amounts of money, which may lead to model bias and fail to effectively identify risky customers.

[0052] In order to solve the above technical problems, this application provides a data processing method. On the one hand, it modifies the three elements in the RFM model (that is, the modified three elements include: the time interval between the moment when the user's consumption amount after registration meets the preset amount and the preset time, the frequency of the user's first product purchase, and the user's total consumption amount within the preset time period), thereby reducing the credit risk caused by the user's short registration time or incomplete external information coverage. On the other hand, by applying the modified RFM to credit risk control scenarios, it is more suitable for determining the payment model of small and micro enterprises that purchase cloud services and pay back the money after payment.

[0053] FIG1A is a schematic diagram of the structure of the data processing system provided by the present application, which includes a terminal device (FIG1A only takes the terminal device as an example of a mobile phone) and a data processing device. It is understandable that, in addition to being a mobile phone, the terminal device can also be a tablet computer (pad), a portable game console, a personal digital assistant (PDA), a laptop computer, an ultra mobile personal computer (UMPC), a handheld computer, a netbook, a vehicle-mounted media player, a wearable electronic device, a virtual reality (VR) terminal device, an augmented reality (AR), a vehicle, a vehicle-mounted terminal, an aircraft terminal, an intelligent robot, and other terminal devices. The terminal device is the initiator of data processing. As the initiator of the data processing request, the request is usually initiated by the user through the terminal device.

[0054] The above-mentioned data processing device can be a device or server with data processing capabilities such as a cloud server, a network server, an application server, and a management server. The data processing device receives a data processing request from a terminal device through an interactive interface, and then processes data based on user information through a memory that stores data and a processor link for data processing (for example, it can be calling an external interface to obtain corporate public opinion, external credit, etc. of the user corresponding to the data processing request). The memory in the data processing device can be a general term, including local storage and a database for storing historical data. The database can be on a cloud device or on other network servers.

[0055] In the data processing system shown in FIG1A , the terminal device can receive instructions from the user. For example, the terminal device can obtain a first request input by the user, and then forward the first request to the data processing device, so that the data processing device executes the data processing method provided in this application for the first request to obtain credit information. Of course, after receiving the first request, the terminal device can forward the user's consumption information, browsing information, etc. on the terminal device together with the first request to the data processing device if the user confirms authorization. Furthermore, the data processing device can refer to more valuable information in the process of determining the credit information for the first request to improve the rationality of the credit information.

[0056] FIG1B is another schematic diagram of the structure of the data processing system provided by the present application. In FIG1B , a terminal device ( FIG1B only uses a mobile phone as an example) can directly execute the data processing method provided by the present application. That is, the terminal device can directly process the first request. The specific process is similar to that in FIG1A , and reference can be made to the above description, which will not be repeated here.

[0057] Optionally, in the data processing system shown in Figure 1B, the terminal device can receive the user's instructions, for example, the terminal device can obtain the user's first request, and then the terminal device itself executes the data processing method provided in this application for the first request to obtain credit information.

[0058] The processor in Figures 1A and 1B can obtain user information based on the first request (for example, the user information can be carried in the first request. For another example, the processor can retrieve user information based on the first request) and use the user information to execute the data processing method provided in this application to obtain corresponding credit information.

[0059] It is understood that, in addition to being the aforementioned physical devices, data processing devices and / or terminal devices may also be implemented as at least one computing instance in a virtual machine or container. When a data processing device is implemented as a virtual machine or container, the data processing device effectively exists as a cloud computing product, capable of providing cloud services. Furthermore, a data processing device may be implemented as multiple computing instances of the same type. For example, a data processing device may be implemented by multiple physical hosts, multiple virtual machines, or multiple containers.

[0060] It should be noted that multiple compute instances can be distributed in the same region or in different regions. Furthermore, multiple compute instances can be distributed in the same availability zone (AZ) or in different AZs, with each AZ including one data center or multiple geographically close data centers. Typically, a region can include multiple AZs.

[0061] Similarly, multiple compute instances can be distributed within the same virtual private cloud (VPC) or across multiple VPCs. Typically, a VPC is set up within a region. Cross-region communication between two VPCs within the same region, or between VPCs in different regions, requires a communication gateway within each VPC to interconnect the VPCs.

[0062] The system architecture provided in this application is described above. The following describes the different stages of each device in the above system architecture in the credit granting scenario in conjunction with Figure 2.

[0063] The credit granting scenario may refer to the scenario in which credit information is determined for a customer. This credit granting information may include at least one of the following: payment mode, the corresponding amount, etc. From the customer's perspective, the aforementioned credit granting scenarios may also include scenarios such as a customer applying for a loan, a customer applying for credit and / or the credit purchase amount, and a customer applying for a credit rating. For ease of description, the following explanation will only use the scenario of a customer applying for credit as an example.

[0064] Furthermore, the stages in a credit granting scenario can primarily include at least one of the following: pre-approval, in-process control, and post-event tracking. At least one of these stages can be applied to the system architecture shown in Figure 1A or Figure 1B. The following describes each stage using the system architecture shown in Figure 1A as an example. These three stages can implement a complete risk management system for the entire lifecycle.

[0065] During the pre-admission phase, payment models can be determined for new and existing customers. Existing customers can be defined as those with a registration duration exceeding a preset duration, a transaction history exceeding a preset number, or a transaction amount exceeding a preset amount, etc. The specifics are not limited here.

[0066] For example, a customer browses products on an application or a web page through a terminal device, and selects a post-payment mode when paying for the selected product. It can also be understood that the customer's action of selecting a payment mode can trigger the terminal device to send a first request to the data processing device. The first request carries customer information. The customer information includes at least a customer identifier. This allows the data processing device to identify the customer based on the customer identifier. In addition, customer information may also include other information about the customer (for example, consumption records, records of browsing products, etc.). After receiving the first request, the data processing device can identify the customer based on the customer identifier carried in the first request. Then, the following judgment can be made:

[0067] For new customers, the data processing device can first access the new customer's public opinion information (e.g., legal proceedings, operating risks, debt information, etc.) to make a judgment. If the new customer does not meet the initial credit requirements, a prepayment model (also known as a prepaid model) is adopted for the new customer. If the new customer meets the initial credit requirements, the external credit rating can be further determined. If the new customer's external credit rating does not pass or is rated low, a prepayment model is adopted for the new customer. If the new customer's external credit rating passes or is rated high, the credit determination determines whether the new customer will adopt a postpayment model (e.g., determining the grant amount for the new customer, determining the postpayment model, etc.). The initial credit requirements may include at least one of the following: whether the new customer's overall capabilities meet the requirements, whether the new customer has negative information (e.g., the new customer's debt exceeds a threshold, the new customer has been blacklisted by other product providers, etc.). For example, if the new customer is an enterprise, the enterprise's overall capabilities may include the enterprise's operating capabilities, product capabilities, brand quality, and enterprise profit prospects. Accordingly, taking operating capabilities as an example, if the enterprise's product sales volume exceeds a threshold, the new customer is determined to meet the credit requirements. Of course, if the new customer is a start-up or a company that is raising funds, the requirements can be relaxed based on the actual situation.

[0068] For old customers, the data processing equipment can first call the public opinion information of the old customers for judgment. If the old customer meets the credit requirements, the prepayment model will be adopted for the old customer. If the old customer does not meet the credit requirements, the internal credit model (which can also be understood as the adjusted RFM) can be further judged. If the output of the old customer's internal credit model fails, the prepayment model will be adopted for the old customer. If the output of the old customer's internal credit model passes, the credit is confirmed and the postpayment model will be adopted for the old customer. Among them, the credit requirements of old customers can include not only the credit requirements of the aforementioned new customers, but also factors such as the old customer's previous application records and whether subsequent payments are made on time.

[0069] It is understood that the processes in the above analysis process can be executed solely by the data processing device or jointly by the data processing device and the terminal device. For example, the terminal device can perform public opinion analysis on the customer and feed back the public opinion information or analysis results to the data processing device. For new customers, the data processing device then performs external credit rating analysis. For existing customers, the data processing device then uses an internal credit model for analysis. For another example, in a credit granting scenario for a new customer, the data processing device performs public opinion analysis on the customer, and the terminal device performs external credit rating analysis on the customer, and feeds back the external credit rating or analysis results to the data processing device. The data processing device can then combine the public opinion information and external credit rating to determine the payment model for the new customer. For another example, in a credit granting scenario for an existing customer, the data processing device performs public opinion analysis on the customer, and the terminal device performs internal credit model analysis on the customer, and feeds back the results of the internal credit model analysis to the data processing device. The data processing device can then combine the public opinion information and internal credit model analysis results to determine the payment model for the existing customer.

[0070] The in-process control stage and the post-event tracking stage can be understood as the process of updating credit for new or old customers.

[0071] After the data processing device determines the customer's payment mode during the pre-access phase, the judgment of the in-process control phase can be triggered after a period of time. For example, the data processing device judges the customer's public opinion information again. If the customer has negative information (or is understood as a hit), an alarm or risk intervention (for example, credit limit adjustment, change to pre-payment mode, etc.) is triggered. If the customer has no negative information, the user's score is determined according to the transaction rating model. If the user's score is greater than or equal to the threshold, the credit is circulated. If the user's score is less than the threshold, an alarm or risk intervention (for example, credit limit adjustment, change to pre-payment mode, etc.) is triggered.

[0072] After the in-process control phase, data processing equipment can trigger a post-event tracking phase at intervals. This post-event tracking phase monitors the risk of assets that have already passed the observation period, predicts the risk of assets that have not yet passed the observation period, calculates migration rates for specific periods, and then uses time-series prediction algorithms to predict and track default and loss rates. Based on the trends in bad debts, risk exposure can be dynamically tightened or relaxed, enabling proactive disposal and intervention. The specific period refers to the specific period of time for a group of loans or credit products. Typically, this period can be the specific loan disbursement period or credit product issuance period, or a specific period, such as each month or each quarter. This specific period can be set based on actual needs and is not limited here. Risk exposure can also be understood as unprotected risk. Unprotected risk can be quantified in monetary terms, for example, the credit balance that may be at risk due to debtor default.

[0073] It is understandable that the public opinion information judgment factors shown in Figure 2 can be more or fewer, and are not specifically limited here.

[0074] For example, companies A, B, and C need to purchase resources such as elastic cloud servers. To better support their sales, the cloud service provider offers a post-payment payment option. Each company applies for credit approval, and the cloud service provider conducts a risk assessment on each of the three companies.

[0075] For example, when a new customer, Enterprise A, submits a credit application, the system retrieves real-time public opinion and negative events about the company and discovers that the company has engaged in dishonest behavior, resulting in restrictions on high-consumption spending. The system intercepts this in advance and denies the credit application, requiring the customer to purchase cloud service resources through a pre-pay model.

[0076] For example, when company B, a new customer, submits a credit application, the system retrieves public opinion and negative events about the company in real time and finds no anomalies. It then retrieves the external rating score and finds it is within the range [a, b], meeting the credit criteria. The system then grants a credit amount of X, initiating the post-payment model.

[0077] For example, when Company C, a new customer, submitted its credit application, the system retrieved the company's public opinion and negative events in real time, but failed to obtain the corresponding information. The customer was required to purchase cloud service resources on a pay-up-front basis.

[0078] The data processing method provided in this application is mainly aimed at the internal credit model in the pre-access stage, which will be explained in conjunction with the accompanying drawings and will not be expanded here.

[0079] Please refer to Figure 3, which is a flow chart of the data processing method provided by the present application. The method may include steps 301 to 303. Steps 301 to 303 can be performed by a data processing device, or by some components in the data processing device (such as a processor, chip or chip system, etc.), or by a logic module or software that can realize all or part of the functions of the data processing device. The following description is taken as an example of execution by a data processing device. The processing performed by a single execution subject in steps 301 to 303 can also be divided into executions by multiple execution subjects, and these execution subjects can be logically and / or physically separated. Among them, the data processing device can be the data processing device in Figure 1A above, or the terminal device in Figure 1B above, etc. Of course, the method can also be executed by a system consisting of a data processing device and a terminal device (this case can also be understood as the terminal device providing user information to the data processing device, and the data processing device executing the method provided by the present application based on the user information).

[0080] Step 301: The data processing device obtains user information.

[0081] In this application, there are many ways for the data processing device to obtain user information, which can be through user input, by receiving information sent by other devices, or by selecting from a database, etc., and the specific methods are not limited here.

[0082] Optionally, the data processing device receives a first request from a user, where the first request is for applying for a postpaid payment model. Furthermore, the data processing device may determine user information based on the first request. For example, the first request may include user information, or the data processing device may retrieve user information based on the first request.

[0083] Optionally, the data processing device obtains user information by receiving information sent by other devices. For example, the data processing device may first obtain a first request and then send the first request to the other device to obtain user information fed back by the other device. In another example, the data processing device may directly obtain user information sent by the other device, etc., the specific details of which are not limited here.

[0084] For example, a user browses products on an application or webpage through a terminal device and selects the post-payment mode when paying for the selected product. It can also be understood that the user's action of selecting the payment mode can trigger the terminal device to send a first request to the data processing device. This first request carries a user identifier. This allows the data processing device to identify the user based on the user identifier. In addition, the first request can also carry other user information (e.g., consumption records, product browsing records, etc.). After receiving the first request, the data processing device can identify the user based on the user identifier carried in the first request. Furthermore, the user information can be obtained based on the user identifier.

[0085] The user information in this application includes at least one of the following: first information, first quantity, and total consumption (also referred to as the three elements). The first information is the time interval between the time when the user's consumption amount after registration meets the preset amount and the preset time, the first quantity is the frequency of the user's purchase of the first product, and the total consumption is the user's total consumption within the preset time period. Furthermore, the user information can be the user information of a single user or multiple users, and the specific details are not limited here.

[0086] Among them, the above-mentioned user information can also be understood as an adaptation adjustment to the existing RFM in order to apply it to credit risk control scenarios.

[0087] Specifically, R is adjusted to the time interval between the moment when the user's consumption amount after registration meets the preset amount and the preset moment. The preset moment can be set according to actual needs or business experience and other factors. For example, the preset moment can be the moment when the user's most recent consumption after registration, or the moment when the user's most recent consumption amount after registration is greater than the preset amount, or the moment when the user sends the first request, etc. For example, the adjusted R is the time interval between the moment when the user's most recent consumption amount after registration is greater than the preset amount and the preset moment. The adjusted R takes into account the influence of the consumption amount and the cloud service billing standard, and can not only filter out data whose amount does not reach the threshold, but also prevent fraudulent customers on the Internet and scenarios where cloud hosts are purchased but not used through registration restrictions. Registration can refer to the user's registration on the cloud platform, which is a platform for users to purchase cloud service products. Cloud service products include at least one of the following: elastic cloud server, cloud container engine, object storage service, cloud hard disk, virtual private cloud, etc.

[0088] Adjust F to the frequency with which users purchase the first product. This first product can be a cloud service-specific product (or a key cloud service product), such as at least one of the aforementioned cloud service products. The adjusted F takes into account the impact of cloud service-specific products on customer stickiness.

[0089] M is adjusted to the total amount of consumption by the user within a preset time period. The starting time of the preset time period may be the registration time, and the ending time of the preset time period may be the time when the user sends the first request, or other time, etc., which is not limited here.

[0090] For example, if the user's most recent cloud service purchase after registration was on February 20th, and the amount spent was greater than the preset amount, and the preset time was February 25th, then R would be 5 days. For another example, if the user purchased a cloud hard drive 10 times, then F would be 10 times. For another example, if the user's total cloud service purchases after registration were 20,000 yuan, then M would be 20,000 yuan.

[0091] Optionally, to obtain more accurate three factors, at least one of the following strategies may be adopted:

[0092] 1. Delete data with actual consumption amount less than or equal to 0 (such as coupon orders, lottery orders, etc.).

[0093] 2. For subscription orders, first calculate the average monthly order amount (also known as cost allocation) and use it as the monthly order amount. The subscription end date serves as the transaction date. For on-demand orders, the order amount is calculated in the month in which the order ends, with the order end date serving as the transaction date. For example, when processing data for annual / monthly subscriptions, the model results are refreshed on a monthly basis. Suppose a customer purchases an annual subscription in January, and the monthly amount is assumed to be 120,000. The database only records this single purchase, but in reality, this customer consumes and uses the service every month, with a monthly fee set at 10,000. This better aligns with modeling requirements and aligns with business practices.

[0094] 3. If the total monthly spending of a customer is less than N, leave the transaction date for that month blank. Then, find the maximum transaction date for each account. Subtract the maximum transaction date from the end date to get R.

[0095] 4. Remove duplicates by account, transaction date and month, and key product type, then count by account to obtain the transaction frequency F.

[0096] 5. Summarize the order amounts by account dimension to obtain indicator M, etc.

[0097] Step 302: The data processing device determines the user's payment mode based on the user information and a preset threshold.

[0098] After obtaining the user information, the data processing device can determine the user's payment mode based on the user information and a preset threshold. The payment mode includes a prepayment mode (also known as a prepaid mode) and a postpayment mode (credit sales mode).

[0099] Optionally, the preset thresholds include at least one of the following: a first threshold corresponding to R, a second threshold corresponding to F, and a third threshold corresponding to M. The data processing device may determine a first value based on the first information and the first threshold, a second value based on the first quantity and the second threshold, and a third value based on the total consumption amount and the third threshold. A payment mode may then be determined based on the first, second, and third values.

[0100] It is understandable that the above-mentioned preset thresholds can be set to be static or dynamically changing. In addition, the preset thresholds corresponding to different users may be different. Alternatively, it can be understood that the value range of the preset threshold can be adjusted in combination with factors such as the user's credit, the user's type, and the user's business information. Among them, there are many ways to classify user types, and they can be divided according to actual needs. For example, the user type may include at least one of the following: whether it is a start-up enterprise, whether it is a micro-small enterprise, whether it is an enterprise in the financing stage, whether it is a listed company, whether it is a supported enterprise, etc. Business information may include: revenue, scale, financial reports, etc.

[0101] For example, if a user has a high credit rating, the preset threshold corresponding to the user can be appropriately adjusted to relax the user's credit requirements. For example, the first threshold corresponding to the first value can be appropriately increased, the second threshold corresponding to the second value can be decreased, and the third threshold corresponding to the third value can be decreased.

[0102] The setting of the above-mentioned preset thresholds can also be understood as indirectly adjusting user information. For example, reducing the second threshold corresponding to the second value can be understood as reducing the number of times a user purchases a particular product. For another example, reducing the third threshold corresponding to the third value can be understood as reducing the total amount of user spending.

[0103] Optionally, the data processing device may first determine the target customer group to which the user belongs based on the first value, the second value, and the third value, and then determine the payment mode based on the position of the target customer group among all customer groups.

[0104] For example, customers can be stratified using the 80 / 20 principle, selecting the top 20% as premium and high-value customers. These customers are then divided into high and low categories based on the three factors (RFM), with 1 representing high and 0 representing low. This means that customers can be divided into 2 × 2 × 2 = 8 target customer groups based on the three factors. It's understandable that other principles, such as the 70 / 30 principle, are also possible and are not specifically defined here.

[0105] For example, if the first value, second value, and third value are 0 or 1, then if the time interval in the first information is less than or equal to the first threshold, the first value of the first information is 1. If the time interval in the first information is greater than the first threshold, the first value of the first information is 0. If the first quantity is greater than or equal to the second threshold, the second value of the second information is 1. If the first quantity is less than the second threshold, the second value of the second information is 0. If the total consumption amount is greater than or equal to the third threshold, the third value of the third information is 1. If the total consumption amount is less than the third threshold, the third value of the third information is 0.

[0106] It is understood that, when determining the values ​​by comparing the three elements in the user information with the preset thresholds, the data processing device may also combine the indication information to determine the values ​​for the user. This indication information is used to adjust at least one of the following: the first value, the second value, and the third value. For example, if Enterprise A is an important cooperative enterprise, even though its total consumption is less than the third threshold, the third value for Enterprise A can be set to 1, thereby relaxing the credit requirements for Enterprise A.

[0107] The target customer groups in this application may include at least one of the following: important value customers, important retained customers, important development customers, important retained customers, general value customers, general development customers, general retained customers, and general retained customers.

[0108] For example, the relationship between the values ​​of the three elements in the user information and the target customer group can be shown in Table 1:

[0109] Table 1

[0110] Among them, the example of Table 1 can also be shown in Figure 4. For example, the target customer group corresponding to the first value, the second value and the third value are all 1 is an important value customer (i.e., a customer with a recent consumption time, a high consumption frequency of a specific product, and a high total consumption amount). The target customer group corresponding to the first value is 0, the second value is 1 and the third value is 1 is an important retention customer (i.e., a customer with a recent consumption time, a high consumption frequency of a specific product, and a high total consumption amount). The target customer group corresponding to the first value is 1, the second value is 0 and the third value is 1 is an important development customer (i.e., a customer with a recent consumption time, a low consumption frequency of a specific product, and a high total consumption amount). The target customer group corresponding to the first value is 0, the second value is 0 and the third value is 1 is an important retention customer (i.e., a customer with a recent consumption time, a low consumption frequency of a specific product, and a high total consumption amount). The target customer group corresponding to the first value is 1, the second value is 1 and the third value is 0 is an average value customer (i.e., a customer with a recent consumption time, a high consumption frequency of a specific product, but a low total consumption amount). The target customer group corresponding to the first value is 1, the second value is 1 and the third value is 0 is an average development customer (i.e., a customer with a recent consumption time, a low consumption frequency of a specific product, and a low total consumption amount). The target customer group corresponding to the first value of 0, the second value of 1, and the third value of 0 is generally retained customers (i.e., customers with a long time to last consumption, a high frequency of consumption of a specific product, and a low total consumption amount). The target customer group corresponding to the first value of 0, the second value of 0, and the third value of 0 is generally retained customers (i.e., customers with a long time to last consumption, a low frequency of consumption of a specific product, and a low total consumption amount).

[0111] After the data processing device determines the target customer group to which the user belongs, it can determine the payment model based on the target customer group's position within all customer groups. The target customer group's position within all customer groups can reflect the specific high and low correspondences of the three elements. For example, a high level of all three elements corresponds to the first position, while a low level of all three elements corresponds to the last position.

[0112] Optionally, the order of the eight target customer groups is consistent with that listed in Table 1 (i.e., important value customers, important retained customers, important development customers, important retained customers, average value customers, average development customers, average retained customers, and average retained customers). The payment model for customers corresponding to the first N target customer groups is determined to be the post-payment model, and the payment model for customers corresponding to the 8-N target customer groups is determined to be the pre-payment model. N is a positive integer greater than 0 and less than or equal to 8.

[0113] For example, if N=4, the payment mode for important value customers, important retained customers, important development customers, and important retained customers is determined to be the post-payment mode, while the payment mode for average value customers, average development customers, average retained customers, and average retained customers is determined to be the pre-payment mode.

[0114] Similarly, the data processing device is in the process of determining the target customer group to which the user belongs. On the one hand, in addition to determining the payment mode by the position of the target customer group among all customer groups, the user's payment mode can also be determined in combination with the indication information. The indication information is used to adjust the position of the target customer group among all customer groups. For example, taking the above N=4 as an example, Company A is a general value customer and theoretically should be in a pre-payment mode. However, Company A is an important partner, and the data processing device can be instructed by means of indication information to adjust the target customer group to which Company A belongs to forward to an important retained customer, thereby adjusting it to a post-payment mode. It can be understood that the above is only a description using the method of indicating the forward movement of the position as an example. In actual applications, the indication information can also be used to directly adjust the target customer group to which the user belongs, etc., and the specific details are not limited here.

[0115] Optionally, the data processing device can also determine the user's payment model and credit amount based on user information and preset thresholds. Specifically, the credit amount can be related to the user's position in the overall customer group, the magnitude of each value in the three factors, or the user's credit rating information. For example, higher credit amounts are associated with higher customer groups. For another example, higher credit amounts are associated with customer groups with higher total spending, etc., and the specifics are not limited here.

[0116] Step 303: The data processing device presents the payment mode result to the user.

[0117] After determining the payment mode of the user, the data processing device may present the payment mode result to the user.

[0118] Alternatively, the data processing device may directly present the result of the payment mode to the user. The data processing device may also send the result of the payment mode to the terminal device, and the terminal device may present the result of the payment mode to the user.

[0119] Furthermore, the payment mode result can be presented through images, text, audio, or other means, and the specifics are not limited here. The above-mentioned result can be presented explicitly or implicitly. Explicitly, it can be understood as directly presenting the "pay first" or "pay later" mode to the user. Implicitly, it can be understood as, if the user successfully purchases a product on credit, the user's payment mode result is implicitly presented as "pay later." If the user fails to purchase a product on credit, it can be implicitly presented as "pay first" mode.

[0120] For example, a user browses products on an application or website through a terminal device and selects the post-payment mode when paying for the selected product, triggering a first request. This first request carries a user identifier. This allows the data processing device to identify the user based on the user identifier. After receiving the first request, the data processing device can determine the user's payment mode according to the aforementioned steps 301 and 302. The data processing device then provides feedback on the user's payment mode to the terminal device. Upon receiving the user's payment mode, the terminal device can present the payment mode result to the user.

[0121] It is understandable that steps 301 to 303 in this embodiment can be applied in the pre-access stage of the credit risk control system, and can also be applied in the in-process control stage, etc., and are not specifically limited here.

[0122] In this application, a user's payment model is determined by first information containing three elements and a preset threshold. The three elements include: the time interval between the time when the user's consumption after registration meets the preset amount and the preset time, the frequency of the user's first product purchases, and the user's total consumption within the preset time period. This method designs a complete credit risk control system for small and micro enterprises that purchase cloud services and pay for them later. It reduces the financial risks caused by short user registration time or incomplete external information coverage.

[0123] Furthermore, as shown in the aforementioned system architecture in FIG2 , before step 301 of the embodiment shown in FIG3 of the present application, the user's public opinion information and / or external rating information may be obtained. When the public opinion information and / or external rating information meet the preset conditions, the user's payment mode may be determined based on the user information and the preset threshold. Among them, the public opinion information meeting the preset conditions may include at least one of the following: the customer has no legal proceedings, the customer has no operating risks, the customer has no debt situation, the customer has no recent shareholder changes, etc. The external rating information meeting the preset conditions may include at least one of the following: the external rating is excellent, the external rating is not lower than the preset level, etc.

[0124] The data processing method of this application has been described above. The data processing device of this application will now be described. Please refer to Figure 5 for an embodiment of the data processing device of this application. This data processing device can implement the functions of the data processing device in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment. In this application, this data processing device can be the data processing device described in Figure 1A or the terminal device described in Figure 1B. This data processing device includes: an acquisition module 501 and a processing module 502.

[0125] Acquisition module 501 is configured to acquire user information, including first information, a first quantity, and a total consumption amount. The first information is the time interval between the time when the user's consumption amount after registration meets the preset amount and the preset time. The first quantity is the frequency of the user's purchase of the first product. The total consumption amount is the total consumption amount of the user within the preset time period.

[0126] Processing module 502, for determining the user's payment mode based on user information and a preset threshold, wherein the payment mode includes: prepay mode and postpay mode;

[0127] The processing module 502 is further configured to present the payment mode result to the user.

[0128] Wherein, both the acquisition module 501 and the processing module 502 can be implemented by software or hardware. For example, the implementation of the processing module 502 is described below using the processing module 502 as an example. Similarly, the implementation of the acquisition module 501 can refer to the implementation of the processing module 502.

[0129] As an example of a software functional unit, the processing module 502 may include code running on a computing instance. The computing instance may be similar to the description of the data processing device and / or terminal device described later in FIG. 1B , i.e., the computing instance may include at least one of a physical host (computing device), a virtual machine, and a container. For related descriptions, please refer to the previous description and will not be repeated here.

[0130] In addition, as an example of a hardware functional unit, the processing module 502 may include at least one computing device, such as a server. Alternatively, the processing module 502 may be implemented using a central processing unit (CPU), an application-specific integrated circuit (ASIC), or a programmable logic device (PLD). The PLD may be a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), a data processing unit (DPU), a neural network processing unit (NPU), a system on chip (SoC), an offload card, an accelerator card, or any combination thereof.

[0131] In one possible implementation, the preset thresholds include a first threshold, a second threshold, and a third threshold; the processing module 502 is specifically used to determine the first value based on the first information and the first threshold; the processing module 502 is specifically used to determine the second value based on the first quantity and the second threshold; the processing module 502 is specifically used to determine the third value based on the total consumption and the third threshold; the processing module 502 is specifically used to determine the payment mode based on the first value, the second value, and the third value.

[0132] In one possible implementation, the processing module 502 is specifically configured to determine the target customer group to which the user belongs based on the first value, the second value, and the third value; and the processing module 502 is specifically configured to determine the payment mode based on the position of the target customer group among all customer groups.

[0133] In one possible implementation, the first value, the second value, and the third value are 0 or 1; if the time interval is less than or equal to the first threshold, the first value of the first information is 1; if the time interval is greater than the first threshold, the first value of the first information is 0; if the first quantity is greater than or equal to the second threshold, the second value of the second information is 1; if the first quantity is less than the second threshold, the second value of the second information is 0; if the total consumption amount is greater than or equal to the third threshold, the third value of the third information is 1; if the total consumption amount is less than the third threshold, the third value of the third information is 0.

[0134] In one possible implementation, when the first value, the second value, and the third value are all 1, the target customer group is important value customers; when the first value is 0, the second value is 1, and the third value is 1, the target customer group is important retention customers; when the first value, the second value, and the third value are all 0, the target customer group is general retention customers.

[0135] In a possible implementation, the acquisition module 501 is further used to obtain the user's public opinion information; the processing module 502 is specifically used to determine the payment mode based on the user information and the preset threshold if the public opinion information meets the preset conditions.

[0136] In one possible implementation, a data processing device is applied to risk control management in a cloud enterprise scenario.

[0137] In a possible implementation, the acquisition module 501 is specifically configured to receive a first request from a user, where the first request carries user information and is used to apply for a post-payment mode.

[0138] In a possible implementation, the preset time is the time when the first request is issued.

[0139] In a possible implementation, the processing module 502 is specifically configured to determine a payment mode and a credit amount based on user information and a preset threshold.

[0140] In this embodiment, the operations performed by each unit in the data processing device are similar to the description of the data processing device in the embodiments shown in Figures 1A to 4 above, and will not be repeated here.

[0141] In this embodiment, processing module 502 determines the user's payment model based on first information containing three elements and a preset threshold. The three elements include: the time interval between the time when the user's post-registration spending reached the preset amount and the preset time, the frequency with which the user purchased the first product, and the user's total spending within the preset time period. This approach designs a comprehensive credit risk control system for small and micro businesses that purchase cloud services and receive payment afterward. This reduces the financial risks associated with recent user registration or incomplete external information coverage.

[0142] Please refer to Figure 6, which is another schematic structural diagram of the data processing device provided by this application. The data processing device includes a logic circuit 601 and an input / output interface 602. The data processing device can be an integrated circuit, etc.

[0143] The acquisition module 501 shown in FIG5 may be a communication interface, which may be the input / output interface 602 in FIG6 , which may include an input interface and an output interface. Alternatively, the communication interface may be a transceiver circuit, which may include an input interface circuit and an output interface circuit. The processing module 502 shown in FIG5 may be the logic circuit 601 in FIG6 .

[0144] Optionally, the input / output interface 602 is used to obtain user information. The logic circuit 801 is used to determine the user's payment mode based on the user information and a preset threshold.

[0145] The logic circuit 601 and the input / output interface 602 may also execute other steps executed by the data processing device in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

[0146] Optionally, the logic circuit 601 may be a processing device, and the functions of the processing device may be partially or entirely implemented by software. The functions of the processing device may be partially or entirely implemented by software.

[0147] Optionally, the data processing device may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform corresponding processing and / or steps in any one of the method embodiments.

[0148] Alternatively, the data processing device may include only a processor. A memory for storing computer programs is located outside the processing device, and the processor is connected to the memory via circuits / wires to read and execute the computer programs stored in the memory. The memory and processor may be integrated or physically separate.

[0149] Optionally, the data processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processor units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic devices (PLDs), or other integrated chips, or any combination of the above chips or processors.

[0150] Please refer to Figure 7, which shows a data processing device 700 involved in the above embodiments provided in the embodiments of the present application. The data processing device 700 can be the data processing device in Figure 1A or the terminal device in Figure 1B.

[0151] As shown in Figure 7, data processing device 700 includes a bus 702, a processor 704, a memory 706, and a communication interface 708. Processor 704, memory 706, and communication interface 708 communicate with each other via bus 702. Data processing device 700 can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memories in data processing device 700.

[0152] Bus 702 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, for example. Buses may be classified as address buses, data buses, control buses, and the like. For ease of illustration, FIG7 illustrates a single bus line, but this does not imply a single bus or type of bus. Bus 702 may include a path for transmitting information between various components of data processing device 700 (e.g., memory 706, processor 704, and communication interface 708).

[0153] The processor 704 may include any one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0154] The memory 706 may include volatile memory, such as random access memory (RAM). The processor 704 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0155] The memory 706 stores executable program codes, and the processor 704 executes the executable program codes to respectively implement the functions of the aforementioned acquisition module and processing module, thereby implementing the aforementioned data processing method. That is, the memory 706 stores instructions for executing the data processing method.

[0156] The communication interface 708 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the data processing device 700 and other devices or a communication network.

[0157] This application also provides a computing device cluster that implements the functionality of the aforementioned data processing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone.

[0158] Please refer to Figure 8, which is a schematic diagram of the structure of a computing device cluster provided in this application. As shown in Figure 8, the computing device cluster includes at least one data processing device 700. The memory 706 of one or more data processing devices 700 in the computing device cluster may store the same instructions for executing the data processing method.

[0159] In some possible implementations, the memory 706 of one or more data processing devices 700 in the computing device cluster may also store partial instructions for executing the data processing method. In other words, the combination of one or more data processing devices 700 can jointly execute the instructions for executing the data processing method.

[0160] It should be noted that the memory 706 in different data processing devices 700 in the computing device cluster can store different instructions, each for executing a portion of the functions of the data processing apparatus. In other words, the instructions stored in the memory 706 in different data processing devices 700 can implement the functions of one or more of the aforementioned acquisition module and processing module.

[0161] In some possible implementations, one or more computing devices in a computing device cluster may be connected via a network. The network may be a wide area network or a local area network, etc. FIG. 9 shows a possible implementation. FIG. 9 is a schematic structural diagram of another computing device cluster provided by the present application. As shown in FIG. 9 , in a computing device cluster 900, two data processing devices 700A and 700B are connected via a network. Specifically, the connection to the network is made via a communication interface in each computing device. In this type of possible implementation, the memory 706 in the data processing device 700A stores instructions for executing the functions of the acquisition module. At the same time, the memory 706 in the data processing device 700B stores instructions for executing the functions of the processing module.

[0162] It should be understood that the functions of the data processing device 700A shown in FIG9 may also be implemented by multiple data processing devices 700. Similarly, the functions of the data processing device 700B may also be implemented by multiple data processing devices 700.

[0163] Please refer to Figure 10, which is a schematic diagram of the structure of a computer-readable storage medium provided by this application. This application also provides a computer-readable storage medium. In some embodiments, the method disclosed in Figure 3 above can be implemented as computer program instructions encoded in a machine-readable format on a computer-readable storage medium or on other non-transitory media or products.

[0164] 10 schematically illustrates a conceptual partial view of an example computer-readable storage medium including a computer program for executing a computer process on a computing device, arranged in accordance with at least some embodiments presented herein.

[0165] In one embodiment, computer readable storage medium 1000 is provided using signal bearing medium 1001. Signal bearing medium 1001 may include one or more program instructions 1002 that, when executed by one or more processors, may provide the functionality or portions of the functionality described above with respect to FIG.

[0166] In some examples, signal bearing medium 1001 may include computer readable medium 1003 such as, but not limited to, a hard drive, compact disk (CD), digital video disk (DVD), digital tape, memory, ROM or RAM, and the like.

[0167] In some embodiments, the signal-bearing medium 1001 may include a computer-recordable medium 1004, such as, but not limited to, a memory, a read / write (R / W) CD, a R / W DVD, or the like. In some embodiments, the signal-bearing medium 1001 may include a communication medium 1005, such as, but not limited to, a digital and / or analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communication link, a wireless communication link, or the like). Thus, for example, the signal-bearing medium 1001 may be communicated via a wireless form of the communication medium 1005 (e.g., a wireless communication medium conforming to the IEEE 802.X standard or other transmission protocol).

[0168] The one or more program instructions 1002 may be, for example, computer-executable instructions or logic-implemented instructions. In some examples, the computing device may be configured to provide various operations, functions, or actions in response to the program instructions 1002 communicated to the computing device via one or more of computer-readable media 1003, computer-recordable media 1004, and / or communication media 1005.

[0169] It should also be noted that the device embodiments described above are merely illustrative, in which the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided in this application, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines.

[0170] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course can also be implemented by special hardware including application-specific integrated circuits, special CPUs, special memories, special components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits, etc. However, for the present application, software program implementation is a better implementation method in most cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer's floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., and includes a number of instructions to enable a computer device (which can be a personal computer, training equipment, or network equipment, etc.) to execute the methods of each embodiment of the present application.

[0171] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0172] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, training equipment or data center to another website, computer, training equipment or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a training equipment, data center, etc. that includes one or more available media integrations. Available media can be magnetic media, (e.g., floppy disk, hard disk, tape), optical media (e.g., DVD), or semiconductor media (e.g., solid-state drive (SSD)), etc.

Claims

1. A data processing method, characterized in that: The method comprises: Obtaining user information, the user information including first information, a first amount, and a total consumption amount, wherein the first information is the time interval between the time when the user's consumption amount after registration meets the preset amount and the preset time, the first amount is the frequency of the user's purchase of the first product, and the total consumption amount is the total consumption amount of the user within the preset time period; Determining a payment mode of the user based on the user information and a preset threshold, wherein the payment mode includes: a prepayment mode and a postpayment mode; The results of the payment model are presented to the user.

2. The method according to claim 1, characterized in that The preset thresholds include a first threshold, a second threshold, and a third threshold; The determining the payment mode of the user based on the user information and a preset threshold includes: determining a first value based on the first information and the first threshold; determining a second value based on the first number and the second threshold; determining a third value based on the total consumption amount and the third threshold; The payment mode is determined based on the first value, the second value, and the third value.

3. The method according to claim 2, characterized in that The determining the payment mode based on the first value, the second value, and the third value includes: determining a target customer group to which the user belongs based on the first value, the second value, and the third value; The payment mode is determined based on the position of the target customer group among all customer groups.

4. The method according to claim 2 or 3, characterized in that The first value, the second value, and the third value are 0 or 1; If the time interval is less than or equal to the first threshold, the first value of the first information is 1; If the time interval is greater than the first threshold, the first value of the first information is 0; If the first number is greater than or equal to the second threshold, the second value of the second information is 1; if the first number is less than the second threshold, the second value of the second information is 0; If the total consumption amount is greater than or equal to the third threshold, the third value of the third information is 1; if the total consumption amount is less than the third threshold, the third value of the third information is 0.

5. The method according to claim 4, characterized in that When the first value, the second value, and the third value are all 1, the target customer group is a high-value customer; When the first value is 0, the second value is 1, and the third value is 1, the target customer group is an important retained customer; When the first value, the second value, and the third value are all 0, the target customer group is general retention customers.

6. The method according to any one of claims 1 to 5, characterized in that Before obtaining the user information, the method further includes: Obtaining public opinion information of the user; The determining the payment mode of the user based on the user information and a preset threshold includes: If the public opinion information meets the preset conditions, the payment mode is determined based on the user information and the preset threshold.

7. The method according to any one of claims 1 to 6, characterized in that The method is applied to risk control management in cloud enterprise scenarios.

8. The method according to any one of claims 1 to 7, characterized in that The obtaining of user information includes: A first request from the user is received, where the first request carries the user information and is used to apply for the post-payment mode.

9. The method according to claim 8, characterized in that The preset time is the time when the first request is issued.

10. The method according to any one of claims 1 to 9, characterized in that The determining the payment mode of the user based on the user information and a preset threshold includes: The payment mode and credit amount are determined based on the user information and the preset threshold.

11. A data processing device, characterized in that: The data processing device comprises: an acquisition module, configured to acquire user information, the user information including first information, a first amount, and a total amount of consumption, wherein the first information is the time interval between the moment when the user's consumption amount after registration meets a preset amount and a preset time, the first amount is the frequency of the user's purchase of the first product, and the total amount of consumption is the total amount of consumption of the user within a preset time period; A processing module, configured to determine a payment mode of the user based on the user information and a preset threshold, wherein the payment mode includes: a prepayment mode and a postpayment mode; The processing module is further configured to present the result of the payment mode to the user.

12. A data processing device, characterized in that: The method comprises at least one processor coupled to a memory; the at least one processor is configured to execute the method according to any one of claims 1 to 10.

13. A computer program product, characterized in that The method comprises instructions, which, when executed on a computer, cause the computer to perform the method according to any one of claims 1 to 10.

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