Limit generation method and device, equipment, medium and product

Abstract

The invention discloses a quota generation method and device, equipment, a medium and a product, and relates to the technical field of financial security, and the method comprises the steps: obtaining account data through a data processing platform; selecting or determining a rule and a limit factor corresponding to the account data; based on a predetermined scheduling strategy, determining a target limit corresponding to the limit factor according to the rule and account data; according to the target quota corresponding to the quota factor, generating a quota calculation result table for a quota using system to call, so that the quota using system does not need to perform real-time calculation, and the quota recommendation requirement of short response time can be met by calling the quota calculation result table generated by a data processing platform; meanwhile, the target limit corresponding to the limit factor is determined according to the rule and the account data based on the predetermined scheduling strategy, so that the complexity and the fineness of the limit calculation process are ensured, and the accuracy of limit recommendation is considered while the response time is shortened.

Description

Technical Field

[0001] The present application relates to the field of financial security technology, and in particular to limit generation methods, devices, equipment, media and products. Background Art

[0002] As an important means of preventing gambling and fraud, transaction limit control can effectively reduce the property losses of victims and reduce the amount involved in the case. In order to set limits reasonably and take into account the needs of business operations and preventing gambling and fraud, a multi-dimensional limit model with the characteristics of thousands of faces and accurate identification has been established.

[0003] The current quota model is highly complex and requires the introduction of multiple external systems. When online requests are made, data from external systems is obtained in real time and calculated based on the model rules to obtain the recommended quota for the system. The high complexity ensures the rationality and accuracy of the model, but it is difficult to meet the quota recommendation requirements with a short response time.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a quota generation method, device, equipment, medium and product, aiming to meet the quota recommendation needs with short response time.

[0006] To achieve the above objectives, the present application provides a quota generation method, which is applied to a data processing platform and includes:

[0007] Get account data;

[0008] Select or determine the rules and limit factors corresponding to the account data;

[0009] Based on a predetermined scheduling strategy, determining a target limit corresponding to the limit factor according to the rule and the account data;

[0010] A quota calculation result table is generated according to the target quota corresponding to the quota factor, so as to be called by the quota usage system.

[0011] In one embodiment, before the step of calculating the target quota corresponding to the quota factor based on the predetermined scheduling strategy according to the rule and the account data, the step further includes:

[0012] Obtain business rules for different scenarios;

[0013] The business rules of the different scenarios are arranged by a scenario processor to determine the scheduling strategy.

[0014] In one embodiment, the step of determining the target quota corresponding to the quota factor based on the predetermined scheduling strategy and according to the rule and account data includes:

[0015] Determine a rule table according to the scheduling strategy, in combination with the rule and the limit factor, wherein the row of the rule table includes the attribution scenario, each policy in the scheduling strategy and the limit factor, and the column of the rule table includes the judgment condition of the rule;

[0016] According to the rule table, identify in sequence whether the account data meets the judgment condition, and obtain an identification result;

[0017] The quotas corresponding to the quota factors corresponding to the various strategies are determined according to the identification results, and the target quotas corresponding to the quota factors are determined in combination with the relationships among the various strategies.

[0018] In one embodiment, the relationship between the strategies includes a parallel relationship and / or a serial relationship, and the step of determining the target limit corresponding to the limit factor in combination with the relationship between the strategies includes:

[0019] If the relationship between the strategies is a parallel relationship, the target limit is determined jointly according to the limits hit by the strategies;

[0020] If the relationship between the strategies is a serial relationship, the limit hit by the strategy with the highest priority among the strategies is selected as the target limit.

[0021] In addition, to achieve the above purpose, the present application also proposes a quota generation method, which is applied to a quota usage system and includes:

[0022] In response to receiving the current business information, calling the limit calculation result table;

[0023] Determine the recommended limit corresponding to the current business information from the limit calculation result table;

[0024] Among them, the limit calculation result table is generated by the data processing platform by obtaining account data, selecting or determining the rules and limit factors corresponding to the account data, and based on a predetermined scheduling strategy, determining the target limit corresponding to the limit factor according to the rules and account data, and generating the target limit corresponding to the limit factor.

[0025] In one embodiment, before the step of calling the quota calculation result table in response to receiving the current business information, the step further includes:

[0026] The limit calculation result table is downloaded and stored via a file exchange platform.

[0027] In addition, to achieve the above purpose, the present application also proposes a quota generation device, the quota generation device comprising:

[0028] Acquisition module, used to obtain account data;

[0029] A selection module, used to select or determine the rules and limit factors corresponding to the account data;

[0030] A determination module, configured to determine a target limit corresponding to the limit factor based on a predetermined scheduling strategy, the rule and the account data;

[0031] A generation module is used to generate a quota calculation result table according to the target quota corresponding to the quota factor, so as to be called by the quota usage system.

[0032] In addition, to achieve the above objectives, the present application also proposes a quota generation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the quota generation method described above.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the limit generation method described above are implemented.

[0034] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the limit generation method described above are implemented.

[0035] One or more technical solutions proposed in this application have at least the following technical effects:

[0036] The account data is obtained through the data processing platform; the rules and limit factors corresponding to the account data are selected or determined; based on a predetermined scheduling strategy, the target limit corresponding to the limit factor is determined according to the rules and account data; a limit calculation result table is generated according to the target limit corresponding to the limit factor for the limit usage system to call, so that the limit usage system does not need to perform real-time calculations, and the limit recommendation requirements with short response time can be met by calling the limit calculation result table generated by the data processing platform. At the same time, based on a predetermined scheduling strategy, the target limit corresponding to the limit factor is determined according to the rules and account data, thereby ensuring the complexity and precision of the limit calculation process, shortening the response time while taking into account the accuracy of the limit recommendation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 A flowchart of the first embodiment of the method for generating a quota for application;

[0040] Figure 2 A flowchart diagram of the second embodiment of the method for generating a quota for application;

[0041] Figure 3 This is a schematic diagram of the overall process of the application limit generation method;

[0042] Figure 4 A schematic diagram of the principle of a quota calculation model according to the third embodiment of the present application;

[0043] Figure 5 A flowchart diagram of the fourth embodiment of the method for generating a quota for application;

[0044] Figure 6 This is a schematic diagram of the structure of the limit generating device for this application;

[0045] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the limit generation method in the embodiment of the present application.

[0046] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0048] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0049] The main solution of the embodiment of the present application is: obtaining account data through a data processing platform; selecting or determining the rules and limit factors corresponding to the account data; based on a predetermined scheduling strategy, determining the target limit corresponding to the limit factor according to the rules and account data; generating a limit calculation result table according to the target limit corresponding to the limit factor for the limit usage system to call, so that the limit usage system does not need to perform real-time calculations, and can meet the limit generation requirements with short response time by calling the limit calculation result table generated by the data processing platform. At the same time, based on a predetermined scheduling strategy, determining the target limit corresponding to the limit factor according to the rules and account data, ensures the complexity and precision of the limit calculation process, and takes into account the accuracy of the limit recommendation while shortening the response time.

[0050] In this embodiment, for the convenience of description, the quota generating device is used as the execution subject for explanation below.

[0051] In the existing technology, transaction limit control is an important means in the prevention of gambling and fraud, which can effectively reduce the property loss of victims and reduce the amount involved. In order to set limits reasonably and take into account the needs of business operations and the prevention of gambling and fraud, a multi-dimensional limit model with the characteristics of thousands of faces and accurate identification has been established.

[0052] The current quota model is highly complex, introducing multiple external systems, obtaining external system data in real time when online requests are made, and combining it with its own model rules for calculation to obtain the recommended quota for the system. The high complexity ensures the rationality and accuracy of the model, but the current method has two problems:

[0053] (1) Long response time: The disadvantage of complex models is longer request links. As the model changes, the response time will continue to increase, which will hinder performance in scenarios with high response time requirements.

[0054] (2) Calculation rules lack aggregation and hierarchy: The current quota model is divided into multiple categories, and the rules of each category are managed separately. There is a lack of aggregation, and it is impossible to uniformly manage and configure the rules. In addition, the model has few levels, and when faced with growing personalized needs, the compatibility is poor, resulting in increased development and testing costs.

[0055] In order to meet the demand for short response time, reducing the complexity of the model is unacceptable in business, and real-time calculation cannot be applied. This application proposes a method for quickly obtaining multi-dimensional model limits using a data warehouse environment to solve the problem of high response time requirements but performance bottlenecks in real-time acquisition. In addition, the calculation rules of the model are aggregated and managed, and classified and hierarchical design is performed, so that there is no need to modify the code script when changing the calculation rules, and rapid changes can be achieved.

[0056] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a limit generation device, etc. The following takes the limit generation device as an example to illustrate this embodiment and the following embodiments.

[0057] Based on this, the present application embodiment provides a method for generating a quota, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the quota generation method of this application.

[0058] In this embodiment, the quota generation method is applied to a data processing platform, including steps S10 to S40:

[0059] Step S10, obtaining account data;

[0060] Specifically, in the embodiment of the present application, the quota calculation is performed through the quota model in the data processing platform. In this process, it is necessary to associate the external system data required for the quota model calculation in the data warehouse environment, and generate a temporary table as the data source for the quota model calculation. Optionally, the data processing platform can be a data warehouse platform, or other non-warehouse data processing platform, such as non-landed streaming data processing, etc. In the embodiment of the present application, the data warehouse platform is mainly used as an example for explanation.

[0061] Optionally, in an embodiment of the present application, the data processing platform first needs to obtain account data related to the limit model calculation. These data may come from different external systems, such as a bank's customer management system, a risk control platform, etc. The data processing platform integrates these data into a temporary table for subsequent processing and analysis.

[0062] Optionally, the data processing platform can obtain account data in batches through the API interface, including account ID, customer age, customer level, monthly average assets, etc. These data are stored in temporary tables in the data warehouse to provide necessary input for the limit model.

[0063] Optionally, in the process of acquiring account data, it is crucial to ensure the accuracy and completeness of the acquired account data, as this will directly affect the accuracy and effectiveness of subsequent limit calculations. The accuracy and completeness of account data can be ensured through data source verification, data verification mechanism and data update strategy. Through data source verification, it can be ensured that all data sources provided by external systems are verified and authorized to avoid the risk of unknown data sources or tampering. Before data is imported into the data warehouse, a data verification mechanism is implemented to check whether the format, type and range of the data meet expectations. For example, check whether the age is a positive number, whether the assets are non-negative, etc. The acquired raw data can be cleaned to remove or correct invalid, erroneous or duplicate data records to ensure data consistency and reliability. By establishing a data update mechanism, it can be ensured that the account data in the data warehouse is up to date and reflects the latest status of the customer.

[0064] Step S20, selecting or determining the rules and limit factors corresponding to the account data;

[0065] Furthermore, after the account data is obtained, the rules and limit factors corresponding to the account data may be selected or determined.

[0066] Optionally, the rule is a specific logic and condition for determining the limit based on the limit factor, and a complete rule consists of a calculation rule and multiple judgment conditions.

[0067] Optionally, limit factors are defined based on specific attributes or behaviors of the account, which are the basic units for limit calculation.

[0068] Optionally, in a data warehouse environment, the account data needs to be associated with the rules and quota factors of the quota model. This usually involves data mapping, that is, mapping the fields of the account data to the parameters of the rules and quota factors.

[0069] Step S30, based on a predetermined scheduling strategy, determining a target limit corresponding to the limit factor according to the rule and the account data;

[0070] Furthermore, after selecting or determining the rules and limit factors corresponding to the account data, the target limit corresponding to the limit factor can be determined based on the rules and the account data based on a predetermined scheduling strategy.

[0071] Optionally, before the step of calculating the target quota corresponding to the quota factor based on the predetermined scheduling strategy according to the rule and the account data, the step further includes:

[0072] Obtain business rules for different scenarios;

[0073] The business rules of the different scenarios are arranged by a scenario processor to determine the scheduling strategy.

[0074] Optionally, the scheduling strategy is a set of rules in the quota model for determining the priority and order of quota calculation in different scenarios. In the embodiment of the present application, firstly, the quota calculation needs to be guided by the scheduling strategy determined by the scenario processor.

[0075] Optionally, the recommended limits calculated for the same account in different scenarios may be different. The scenario processor abstracts the business rules for different scenarios and schedules the limit policy. For example, in the maintenance scenario, the anti-money laundering policy, protective policy, and black, white, and gray policies work together. The black, white, and gray policies are in a serial relationship, and only one of them will be hit. The priority decreases from left to right, while the overall relationship between black, white, and gray and the anti-money laundering and protective policies is parallel. The three work together to get the final target limit.

[0076] Step S40, generating a quota calculation result table according to the target quota corresponding to the quota factor, for the quota usage system to call.

[0077] Furthermore, based on a predetermined scheduling strategy, after determining the target limit corresponding to the limit factor according to the rules and account data, a limit calculation result table can be generated according to the target limit corresponding to the limit factor for the limit usage system to call.

[0078] Optionally, in an embodiment of the present application, the system will generate a limit calculation result table based on the target limit of the limit factor of each account determined. This result table is the final output of the limit model calculation, which contains the limit information of batch accounts. Optionally, the limit calculation result table should have a clear data structure design so that the limit usage system can easily read and parse it. It may include fields such as account identification, daily limit, and annual limit. Before generating the result table, it is necessary to ensure the accuracy and consistency of all limit data. This may involve re-verification and verification of the data to avoid errors in the limit calculation process.

[0079] Optionally, after the data processing platform generates the limit calculation result table, the limit calculation result table can be stored in the file exchange platform. The limit usage system can download the limit calculation result table from the file exchange platform in advance and store it locally, so as to quickly obtain the system's limit recommendation without cross-machine access, greatly shortening the delay caused by communication.

[0080] Through the above scheme, this embodiment obtains account data through a data processing platform; selects or determines the rules and limit factors corresponding to the account data; based on a predetermined scheduling strategy, determines the target limit corresponding to the limit factor according to the rules and account data; generates a limit calculation result table according to the target limit corresponding to the limit factor for the limit usage system to call, so that the limit usage system does not need to perform real-time calculations, and can meet the limit recommendation requirements of short response time by calling the limit calculation result table generated by the data processing platform. At the same time, based on a predetermined scheduling strategy, determines the target limit corresponding to the limit factor according to the rules and account data, thereby ensuring the complexity and precision of the limit calculation process, shortening the response time while taking into account the accuracy of the limit recommendation.

[0081] Based on the first embodiment of the present application, the second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar contents as those of the above-mentioned embodiment 1 can be referred to the above introduction and will not be repeated later. Figure 2 , step S30 includes steps S301 to S303:

[0082] Step S301: determining a rule table according to the scheduling strategy, in combination with the rule and the limit factor, wherein the row of the rule table includes the attribution scenario, each policy in the scheduling strategy and the limit factor, and the column of the rule table includes the judgment condition of the rule;

[0083] Step S302: identifying whether the account data meets the judgment condition in sequence according to the rule table, and obtaining an identification result;

[0084] Step S303: Determine the quota corresponding to the quota factor corresponding to each policy according to the identification result, and determine the target quota corresponding to the quota factor in combination with the relationship between the policies.

[0085] Optionally, the relationship between the strategies includes a parallel relationship and / or a serial relationship.

[0086] Optionally, the step of determining the target quota corresponding to the quota factor in combination with the relationship between the strategies includes:

[0087] If the relationship between the strategies is a parallel relationship, the target limit is determined jointly according to the limits hit by the strategies;

[0088] If the relationship between the strategies is a serial relationship, the limit hit by the strategy with the highest priority among the strategies is selected as the target limit.

[0089] Optionally, in an embodiment of the present application, under the architecture of classified and hierarchical design, a rule table is designed to store different rules in the form of different rows, and the scenarios, strategies, and factors to which they belong are reflected in columns. The judgment conditions and calculation methods of the rules are respectively present in the form of columns. A single rule can be configured by modifying the judgment conditions and calculation methods in the columns, or the rules can be classified by specifying scenarios, strategies, and factors, and freely combined in a rule-configured manner. A complete rule consists of a calculation rule and multiple judgment conditions.

[0090] Optionally, a strategy is to classify a group of factors with similar business characteristics. The strategy can perform logical operations on the factors. For example, the blacklist strategy takes the minimum value from the recommended limits given for all hit factors under the strategy, while the whitelist strategy takes the maximum value from the recommended limits given for all hit factors under the strategy. The relationship between strategies is divided into parallel and serial. Parallel means that strategies can exist at the same time and work together, and serial means a relationship with priority or multiple-choice in business.

[0091] Optionally, in the embodiment of the present application, taking the example of a customer going to a branch to adjust the existing single amount, the limit model can pull a variety of information about the customer, such as the customer's age of 30, the customer level is a high-level customer, the customer's monthly average assets are 500,000, and the customer is not included in the anti-money laundering control list, the protected customer list, the public security blacklist, and other restricted lists. The limit model will judge the anti-money laundering strategy, protective strategy, and black and white gray strategy in turn. If the anti-money laundering, protective strategy, and blacklist strategy are all not hit, then continue to look at the whitelist strategy.

[0092] Refer to Table 1, which is an example table 1 for rule table design, which includes rule codes, strategies, scenarios, factor codes and limit modes, etc. Refer to Table 2, which is an example table 1 for rule configuration. If the limit model is equipped with a rule under the whitelist strategy that sets a daily limit of 2 million and an annual limit of 10 million when the customer's average monthly assets are greater than 300,000, the customer will hit the whitelist strategy, and the graylist strategy will no longer be calculated. The final limits are a daily limit of 2 million and an annual limit of 10 million, which correspond to the daily and annual limits filled in Table 1.

[0093] Table 1. Example of rule table design Table 1

[0094]

[0095]

[0096] Table 2. Example of rule configuration Table 1

[0097] Rule Encoding Condition No. lvalue name Conditional judgement Rvalue constant Rule code 1 1 Average monthly assets Greater than 300,000

[0098] Refer to Table 3, which is the second example of rule table design, including rule coding, strategy, scenario, factor code and limit mode, etc. Refer to Table 4, which is the second example of rule configuration. If the asset condition under the whitelist strategy is greater than 600,000, the whitelist cannot be hit and then the gray list is calculated. There is a rule under the gray list that the age is greater than or equal to 30 years old, the customer level is a high-level customer, the daily limit is 2 times the asset, and the annual limit is 50 times the daily limit. The final limit is 1 million per day and 50 million per year, which corresponds to the daily limit and annual limit filled in Table 3.

[0099] Table 3. Example of rule table design Table 2

[0100]

[0101] Table 4. Rule configuration example table 2

[0102] Rule Encoding Condition No. lvalue name Conditional judgement Rvalue constant Rule code 1 1 Average monthly assets Greater than 600,000 Rule Code 2 1 Customer level equal High-level customers Rule Code 2 2 age Greater than or equal to 30

[0103] Refer to Table 5, which is Example Table 3 of Rule Table Design, which includes rule coding, strategy, scenario, factor code and limit mode, etc. Refer to Table 6, which is Example Table 3 of Rule Configuration. When a customer hits multiple factors at the same time, the customer hits factor code 2 and factor code 3 under the gray list. The calculation logic within the gray list is to take the smaller value, then the final limit is 1 million per day and 20 million per year, which are filled in with the daily limit and annual limit in Table 5.

[0104] Table 5. Example of rule table design Table 3

[0105]

[0106] Table 6. Rule configuration example table 3

[0107] Rule Encoding Condition No. lvalue name Conditional judgement Rvalue constant Rule code 1 1 Average monthly assets Greater than 600,000 Rule Code 2 1 Customer level equal High-level customers Rule Code 2 2 age Greater than or equal to 30 Rule code 3 1 Default hit

[0108] This embodiment adopts the above scheme, specifically by determining a rule table according to the scheduling strategy, in combination with the rules and the limit factors, wherein the rows of the rule table include the attribution scenario, each strategy in the scheduling strategy and the limit factor, and the columns of the rule table include the judgment condition and / or calculation method of the rule; according to the rule table, it is identified in turn whether the account data meets the judgment condition to obtain an identification result; according to the identification result, the limit corresponding to the limit factor corresponding to each strategy is determined, and the target limit corresponding to the limit factor is determined in combination with the relationship between the strategies, thereby realizing the classification and hierarchical design of the limit model, the aggregation management of the rules, the personalized configuration and rapid change of the calculation rules, and retaining the complexity and sophistication of the limit model.

[0109] Based on any of the foregoing embodiments of the present application, a third embodiment of the present application is proposed. In the third embodiment of the present application, the same or similar contents as any of the foregoing embodiments can be referred to the above introduction and will not be described in detail later. Figure 3 , Figure 3 The overall flow chart of the application limit generation method is provided, specifically:

[0110] In an embodiment of the present application, all external system data required for the quota model calculation are associated in a data warehouse environment, a temporary table is generated as the data source for the quota model calculation, a quota rule table and a quota configuration table are designed to aggregate the calculation rules into the table, and all data sources and quota rules are combined to obtain a quota calculation result table after advance calculation, which is then stored in a file exchange platform (also known as a data service platform). Downstream users (i.e., the quota usage system) only need to download the file to the local computer in advance to quickly obtain the recommended quota of the system without cross-machine access, thereby greatly shortening the communication delay.

[0111] Specifically, referring to Table 7, which is an example table of a classification hierarchical structure, in the embodiment of the present application, the logic of limit calculation is split based on business characteristics and decomposed into four levels, each level having multiple categories.

[0112] Table 7. Example of classification hierarchical structure

[0113] Tiers name Function First floor Scene Processor Responsible for arranging scheduling strategies Second floor Strategy Perform logical operations on the limit factors, and the relationship between strategies is divided into parallel and serial Third floor factor Get the calculation limit of the factor based on the configured rules and external data Fourth floor rule Configured rule operations

[0114] Optionally, the quota rule is the minimum granularity of quota calculation. Only with the rule can the recommended quota of a single factor be derived. The quota factor is a basic concept in the quota business. One factor can be configured with multiple rules. Whether it is hit and which one is hit is determined by the rule table. A strategy is to classify a group of factors with similar characteristics in the business. The strategy can perform logical operations on the factors. For example, the blacklist strategy takes the minimum value of the recommended quota given for all hit factors under the strategy, while the whitelist strategy takes the maximum value of the recommended quota given for all hit factors under the strategy. The relationship between strategies is divided into parallel and serial. Parallel means that strategies can exist at the same time and work together, and serial means that there is a priority or multiple-choice relationship in the business.

[0115] Optionally, the recommended limits calculated for the same account in different scenarios may be different. The scenario processor abstracts the business rules for different scenarios and schedules the limit policy. Figure 4 , Figure 4 FIG. 1 is a schematic diagram showing a limit calculation model principle according to the third embodiment of the present application. Figure 4As shown, in the maintenance scenario, the anti-money laundering strategy, protective strategy, and black, white, and gray strategy work together. The black, white, and gray strategies are in a serial relationship, and only one of them will be hit. The priority decreases from left to right, and the relationship between the black, white, and gray as a whole and the anti-money laundering and protective strategies is parallel. The three work together to get the final limit.

[0116] Optionally, under the architecture of classified and hierarchical design, a rule table is designed to store different rules in the form of different rows, and the scenarios, strategies, and factors to which they belong are reflected in columns. The judgment conditions and calculation methods of the rules are respectively present in the form of columns. A single rule can be configured by modifying the judgment conditions and calculation methods in the columns, or the rules can be classified by specifying scenarios, strategies, and factors, and freely combined in a rule-configured manner.

[0117] Based on any of the foregoing embodiments of the present application, a fourth embodiment of the present application is proposed. In the fourth embodiment of the present application, the same or similar contents as any of the foregoing embodiments can be referred to the above introduction and will not be described in detail later. Figure 5 In this embodiment, the quota generation method is applied to the quota usage system, including steps A10 to A20:

[0118] Step A10, in response to receiving the current business information, calling the limit calculation result table;

[0119] Step A20, determining the recommended quota corresponding to the current business information from the quota calculation result table.

[0120] Optionally, the limit calculation result table is generated by the data processing platform by acquiring account data, selecting or determining rules and limit factors corresponding to the account data, determining the target limit corresponding to the limit factor based on the rules and account data based on a predetermined scheduling strategy, and generating the target limit corresponding to the limit factor.

[0121] Optionally, the step of calling the limit calculation result table includes:

[0122] Downloading and storing the limit calculation result table through a file exchange platform;

[0123] In response to receiving the current service information, a recommended quota corresponding to the current service information is determined from the quota calculation result table.

[0124] Optionally, in an embodiment of the present application, quota calculation is performed through a quota model in a data processing platform. During this process, it is necessary to associate external system data required for quota model calculation in a data warehouse environment and generate a temporary table as a data source for quota model calculation.

[0125] Optionally, in an embodiment of the present application, the data processing platform first needs to obtain account data related to the limit model calculation. These data may come from different external systems, such as a bank's customer management system, a risk control platform, etc. The data processing platform integrates these data into a temporary table for subsequent processing and analysis.

[0126] Optionally, the data processing platform can obtain account data in batches through the API interface, including account ID, customer age, customer level, monthly average assets, etc. These data are stored in temporary tables in the data warehouse to provide necessary input for the limit model.

[0127] Optionally, in the process of acquiring account data, it is crucial to ensure the accuracy and completeness of the acquired account data, as this will directly affect the accuracy and effectiveness of subsequent limit calculations. The accuracy and completeness of account data can be ensured through data source verification, data verification mechanism and data update strategy. Through data source verification, it can be ensured that all data sources provided by external systems are verified and authorized to avoid the risk of unknown data sources or tampering. Before data is imported into the data warehouse, a data verification mechanism is implemented to check whether the format, type and range of the data meet expectations. For example, check whether the age is a positive number, whether the assets are non-negative, etc. The acquired raw data can be cleaned to remove or correct invalid, erroneous or duplicate data records to ensure data consistency and reliability. By establishing a data update mechanism, it can be ensured that the account data in the data warehouse is up to date and reflects the latest status of the customer.

[0128] Optionally, the rule is a specific logic and condition for determining the limit based on the limit factor, and a complete rule consists of a calculation rule and multiple judgment conditions.

[0129] Optionally, limit factors are defined based on specific attributes or behaviors of the account, which are the basic units for limit calculation.

[0130] Optionally, in a data warehouse environment, the account data needs to be associated with the rules and quota factors of the quota model. This usually involves data mapping, that is, mapping the fields of the account data to the parameters of the rules and quota factors.

[0131] Optionally, the scheduling strategy is a set of rules in the quota model for determining the priority and order of quota calculation in different scenarios. In the embodiment of the present application, firstly, the quota calculation needs to be guided by the scheduling strategy determined by the scenario processor.

[0132] Optionally, the recommended limits calculated for the same account in different scenarios may be different. The scenario processor abstracts the business rules for different scenarios and schedules the limit policy. For example, in the maintenance scenario, the anti-money laundering policy, protective policy, and black, white, and gray policies work together. The black, white, and gray policies are in a serial relationship, and only one of them will be hit. The priority decreases from left to right, while the overall relationship between black, white, and gray and the anti-money laundering and protective policies is parallel. The three work together to get the final target limit.

[0133] Optionally, in an embodiment of the present application, the system will generate a limit calculation result table based on the target limit of the limit factor of each account determined. This result table is the final output of the limit model calculation, which contains the limit information of batch accounts. Optionally, the limit calculation result table should have a clear data structure design so that the limit usage system can easily read and parse it. It may include fields such as account identification, daily limit, and annual limit. Before generating the result table, it is necessary to ensure the accuracy and consistency of all limit data. This may involve re-verification and verification of the data to avoid errors in the limit calculation process.

[0134] Optionally, after the data processing platform generates the limit calculation result table, the limit calculation result table can be stored in the file exchange platform. The limit usage system can download the limit calculation result table from the file exchange platform in advance and store it locally, so as to quickly obtain the system's recommended limit without cross-machine access, greatly shortening the delay caused by communication.

[0135] In the embodiment of the present application, the quota model is calculated in advance using a data warehouse, and the user only needs a short response time to obtain the recommended quota of the quota model, which minimizes the impact of the quota model on the user, and the iteration of the quota model no longer affects the performance indicator of response time. By classifying and layering the quota model, the quota rules can be configured at the smallest granularity, and the quota rule changes or new quota rules do not require code modification, achieving rapid delivery.

[0136] This embodiment uses the above scheme, specifically through the limit usage system calling the limit calculation result table, wherein the limit calculation result table is obtained by the data processing platform through the account data, selecting or determining the rules and limit factors corresponding to the account data, and based on the predetermined scheduling strategy, determining the target limit corresponding to the limit factor according to the rules and account data, and generating according to the target limit corresponding to the limit factor, so that the limit usage system does not need to perform real-time calculations, and can meet the limit recommendation requirements of short response time by calling the limit calculation result table generated by the data processing platform. At the same time, based on the predetermined scheduling strategy, the target limit corresponding to the limit factor is determined according to the rules and account data, ensuring the complexity and precision of the limit calculation process, while shortening the response time while taking into account the accuracy of the limit recommendation.

[0137] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the limit generation method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0138] This application also provides a limit generation device, please refer to Figure 6 The quota generating device is applied to a data processing platform and includes:

[0139] Acquisition module, used to obtain account data;

[0140] A selection module, used to select or determine the rules and limit factors corresponding to the account data;

[0141] A determination module, configured to determine a target limit corresponding to the limit factor based on a predetermined scheduling strategy, the rule and the account data;

[0142] A generation module is used to generate a quota calculation result table according to the target quota corresponding to the quota factor, so as to be called by the quota usage system.

[0143] The quota generation device provided by the present application adopts the quota generation method in the above embodiment, which can solve the technical problem of quota generation. Compared with the prior art, the beneficial effects of the quota generation device provided by the present application are the same as the beneficial effects of the quota generation method provided by the above embodiment, and other technical features in the quota generation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0144] The present application provides a quota generation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the quota generation method in the above-mentioned embodiment one.

[0145] Reference below Figure 7 , which shows a schematic diagram of the structure of a quota generation device suitable for implementing the embodiment of the present application. The quota generation device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The quota generating device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0146] like Figure 7 As shown, the quota generating device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the quota generating device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the quota generating device to communicate wirelessly or wired with other devices to exchange data. Although the quota generating device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0147] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0148] The quota generation device provided by the present application adopts the quota generation method in the above embodiment, which can solve the technical problem of quota generation. Compared with the prior art, the beneficial effects of the quota generation device provided by the present application are the same as the beneficial effects of the quota generation method provided by the above embodiment, and the other technical features in the quota generation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0149] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0150] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0151] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the quota generation method in the above-mentioned embodiment.

[0152] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0153] The above-mentioned computer-readable storage medium may be included in the quota generating device; or it may exist independently without being assembled into the quota generating device.

[0154] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the quota generating device, the quota generating device: obtains account data through a data processing platform; selects or determines the rules and quota factors corresponding to the account data; based on a predetermined scheduling strategy, determines the target quota corresponding to the quota factor according to the rules and account data; generates a quota calculation result table according to the target limit corresponding to the quota factor, for the quota usage system to call.

[0155] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0156] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0157] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0158] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned quota generation method, and can solve the technical problem of quota generation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the quota generation method provided in the above-mentioned embodiment, and will not be repeated here.

[0159] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned limit generation method when executed by a processor.

[0160] The computer program product provided by this application can solve the technical problem of quota generation. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the quota generation method provided by the above embodiment, which will not be repeated here.

[0161] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A quota generation method, characterized in that: The method is applied to a data processing platform, comprising: Get account data; Select or determine the rules and limit factors corresponding to the account data; Based on a predetermined scheduling strategy, determining a target limit corresponding to the limit factor according to the rule and the account data; A quota calculation result table is generated according to the target quota corresponding to the quota factor, so as to be called by the quota usage system.

2. The method according to claim 1, characterized in that The step of calculating the target quota corresponding to the quota factor based on the predetermined scheduling strategy according to the rules and account data also includes: Obtain business rules for different scenarios; The business rules of the different scenarios are arranged by a scenario processor to determine the scheduling strategy.

3. The method according to claim 2, characterized in that The step of determining the target limit corresponding to the limit factor based on the predetermined scheduling strategy and according to the rules and account data includes: Determine a rule table according to the scheduling strategy, in combination with the rule and the limit factor, wherein the row of the rule table includes the attribution scenario, each policy in the scheduling strategy and the limit factor, and the column of the rule table includes the judgment condition of the rule; According to the rule table, identify in sequence whether the account data meets the judgment condition, and obtain an identification result; The quotas corresponding to the quota factors corresponding to the various strategies are determined according to the identification results, and the target quotas corresponding to the quota factors are determined in combination with the relationships among the various strategies.

4. The method according to claim 3, characterized in that The relationship between the strategies includes a parallel relationship and / or a serial relationship. The step of determining the target limit corresponding to the limit factor in combination with the relationship between the strategies includes: If the relationship between the strategies is a parallel relationship, the target limit is determined jointly according to the limits hit by the strategies; If the relationship between the strategies is a serial relationship, the limit hit by the strategy with the highest priority among the strategies is selected as the target limit.

5. A quota generation method, characterized in that: The method is applied to a quota usage system, comprising: In response to receiving the current business information, calling the limit calculation result table; Determine the recommended limit corresponding to the current business information from the limit calculation result table; Among them, the limit calculation result table is generated by the data processing platform by obtaining account data, selecting or determining the rules and limit factors corresponding to the account data, and based on a predetermined scheduling strategy, determining the target limit corresponding to the limit factor according to the rules and account data, and generating the target limit corresponding to the limit factor.

6. The method according to claim 5, characterized in that In response to receiving the current business information, before the step of calling the limit calculation result table, the step further includes: The limit calculation result table is downloaded and stored via a file exchange platform.

7. A quota generating device, characterized in that: The device is applied to a data processing platform and comprises: Acquisition module, used to obtain account data; A selection module, used to select or determine the rules and limit factors corresponding to the account data; A determination module, configured to determine a target limit corresponding to the limit factor based on a predetermined scheduling strategy, the rule and the account data; A generation module is used to generate a quota calculation result table according to the target quota corresponding to the quota factor, so as to be called by the quota usage system.

8. A quota generating device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the quota generation method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the quota generation method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the quota generation method according to any one of claims 1 to 6 are implemented.

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