Data processing method and device, electronic device and computer readable storage medium

Abstract

The present disclosure provides a data processing method, which can be used in the field of big data technology and also in the field of financial technology. The method includes: receiving original request data from an installment business request end; verifying the original request data according to a preset verification rule, so as to determine the original request data that passes the verification as target basic information data, wherein the target basic information data is data that conforms to a preset data format and a preset data type; obtaining target risk screening data; and sending the target risk screening data and the target basic information data to a decision engine, so that the decision engine performs data processing according to the target risk screening data and the target basic information data, and then returns the original result data related to the decision result to the data processing system. The present disclosure also provides a data processing device, an electronic device, a computer-readable storage medium, and a computer program product.

Description

Technical Field

[0001] The present disclosure relates to the field of big data technology, and more specifically, to a data processing method and device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the rapid development of the Internet financial industry today, more and more installment demands are coming, including cash installment, E installment, car installment, home improvement installment, etc. After the customer submits the installment application, the financial institution needs to review and approve it.

[0003] In the process of realizing the concept of the present disclosure, the inventors found that there are at least the following problems in the related technology: different installment business lines need to be approved separately, and different installment products need to request data interactively separately, resulting in poor maintainability and scalability. Summary of the invention

[0004] In view of this, the present disclosure provides a data processing method and apparatus, an electronic device, a computer-readable storage medium, and a computer program product.

[0005] One aspect of the present disclosure provides a data processing method, comprising:

[0006] Receiving original request data from the installment business request end, the original request data including original customer information data and original business information data, wherein the original business information data is related to the installment product business type;

[0007] Verifying the original request data according to a preset verification rule, so as to determine the original request data that passes the verification as the target basic information data, wherein the target basic information data is data that conforms to a preset data format and a preset data type;

[0008] Obtain target risk screening data, wherein the target risk screening data is risk screening data that matches the target risk screening data type, and the target risk screening data type is the risk screening data type on which the decision engine performs a decision;

[0009] The target risk screening data and the target basic information data are sent to the decision engine so that the decision engine performs data processing based on the target risk screening data and the target basic information data, and then returns the original result data related to the decision result to the data processing system.

[0010] According to an embodiment of the present disclosure, the preset verification rules include a first preset verification rule and a second preset verification rule, wherein the first preset verification rule is used to determine whether the data type of the original request data conforms to the preset data type, and the second preset verification rule is used to determine whether the data format of the original request data conforms to the preset data format, wherein the preset data type is the request data type on which the decision engine executes the decision, and the preset data format is the data format that needs to be satisfied when data is stored in the database of the data processing system.

[0011] According to an embodiment of the present disclosure, the first preset verification rule includes a general customer information verification rule and multiple unique business information verification rules, wherein the general customer information verification rule is used for data type verification of original customer information data under various installment product business types, and the multiple unique business information verification rules are respectively used for data type verification of original business information data under each installment product business type.

[0012] According to an embodiment of the present disclosure, wherein:

[0013] The preset data types include a first preset data type for characterizing the target data type of original customer information data under various installment product business types, and a second preset data type for characterizing the target data type of original business information data under each installment product business type, wherein the first preset data type is the customer information data type on which the decision engine executes decisions; and wherein the second preset data type is the business information data type on which the decision engine executes decisions.

[0014] Verifying the original request data according to the preset verification rules includes:

[0015] Verify, according to the general customer information verification rule, whether the data type of the customer information data under various installment product business types conforms to the first preset data type;

[0016] Verifying whether the data type of the original business information data under each installment product business type complies with the second preset data type according to each unique business information verification rule;

[0017] The second preset verification rule is used to verify whether the data formats of the customer information data and the original business information data conform to the preset data format.

[0018] According to an embodiment of the present disclosure, verifying the original request data according to a preset verification rule includes:

[0019] Add custom annotations to the original request data;

[0020] Through JAVA reflection, using custom annotations and preset verification rules, each data in the original request data is verified separately.

[0021] According to an embodiment of the present disclosure, obtaining target risk screening data includes:

[0022] Obtain basic risk screening data based on the target basic information data, wherein the basic risk screening data is all types of risk screening data that match the business type of the installment product;

[0023] Data matching the target risk screening data type are filtered from the basic risk screening data to obtain the target risk screening data.

[0024] According to an embodiment of the present disclosure, obtaining basic risk screening data according to target basic information data includes:

[0025] Analyze the target basic information data to obtain the business type of installment products;

[0026] Obtain basic information data on risk screening related to the business type of installment products;

[0027] Obtain basic risk screening data based on risk screening basic information data.

[0028] According to an embodiment of the present disclosure, screening target risk data and sending it to a decision engine includes:

[0029] Through JAVA reflection, each field information of the target risk screening data and the target basic information data is matched one by one with the first target field information, wherein the first target field information is the field information of the data on which the decision engine executes the decision.

[0030] According to an embodiment of the present disclosure, it also includes:

[0031] Parsing raw result data related to the decision result to obtain the decision result data;

[0032] The decision result data is stored in the database of the data processing system.

[0033] According to an embodiment of the present disclosure, storing the decision result data in a database of a data processing system includes:

[0034] By means of JAVA reflection, each field information of the decision result data is matched one by one with the second target field information, wherein the second target field information is the field information used when the database stores the decision result data.

[0035] According to an embodiment of the present disclosure, it also includes:

[0036] The decision result is read from the database at a preset time interval so as to return the decision result to the installment business request end.

[0037] Another aspect of the present disclosure provides a data processing device, including a receiving module, a checking module, an acquiring module and a sending module.

[0038] The receiving module is used to receive original request data from the installment business request end, and the original request data includes original customer information data and original business information data, wherein the original business information data is related to the installment product business type.

[0039] A verification module is used to verify the original request data according to a preset verification rule, so as to determine the original request data that passes the verification as the target basic information data, wherein the target basic information data is data that conforms to a preset data format and a preset data type,

[0040] The acquisition module is used to obtain target risk screening data, wherein the target risk screening data is risk screening data that matches the target risk screening data type, and the target risk screening data type is the risk screening data type on which the decision engine executes decisions.

[0041] The sending module is used to send the target risk screening data and the target basic information data to the decision engine, so that the decision engine performs data processing based on the target risk screening data and the target basic information data, and then returns the original result data related to the decision result to the data processing system.

[0042] According to an embodiment of the present disclosure, the preset verification rules include a first preset verification rule and a second preset verification rule, wherein the first preset verification rule is used to determine whether the data type of the original request data conforms to the preset data type, and the second preset verification rule is used to determine whether the data format of the original request data conforms to the preset data format, wherein the preset data type is the request data type on which the decision engine executes the decision, and the preset data format is the data format that needs to be satisfied when data is stored in the database of the data processing system.

[0043] According to an embodiment of the present disclosure, the first preset verification rule includes a general customer information verification rule and multiple unique business information verification rules, wherein the general customer information verification rule is used for data type verification of original customer information data under various installment product business types, and the multiple unique business information verification rules are respectively used for data type verification of original business information data under each installment product business type.

[0044] According to an embodiment of the present disclosure, the preset data type includes a first preset data type for representing a target data type of original customer information data under various installment product business types, and a second preset data type for representing a target data type of original business information data under each installment product business type, wherein the first preset data type is a customer information data type based on which the decision engine performs a decision; wherein the second preset data type is a business information data type based on which the decision engine performs a decision;

[0045] The verification module includes a first verification unit, a second verification unit and a third verification unit.

[0046] Among them, the first verification unit is used to verify whether the data type of customer information data under various installment product business types conforms to the first preset data type according to the general customer information verification rules; the second verification unit is used to verify whether the data type of the original business information data under each installment product business type conforms to the second preset data type according to each unique business information verification rule; the third verification unit is used to verify whether the data format of the customer information data and the original business information data conforms to the preset data format according to the second preset verification rule.

[0047] According to an embodiment of the present disclosure, the above-mentioned verification module includes an adding unit and a fourth verification unit.

[0048] Among them, the adding unit is used to add custom annotations to the original request data; the fourth verification unit is used to verify each data in the original request data separately by using JAVA reflection, custom annotations and preset verification rules.

[0049] According to an embodiment of the present disclosure, the acquisition module includes an acquisition unit and a screening unit.

[0050] The acquisition unit is used to acquire basic risk screening data based on the target basic information data, wherein the basic risk screening data is all types of risk screening data that match the business type of the installment product; the screening unit is used to screen data that matches the target risk screening data type from the basic risk screening data to obtain the target risk screening data.

[0051] According to an embodiment of the present disclosure, the acquisition unit includes a parsing subunit, a first acquisition subunit, and a second acquisition subunit.

[0052] Among them, the parsing subunit is used to parse the target basic information data to obtain the installment product business type; the first acquisition subunit is used to obtain the risk screening basic information data related to the installment product business type; the second acquisition subunit is used to obtain basic risk screening data based on the risk screening basic information data.

[0053] According to an embodiment of the present disclosure, in the above-mentioned sending module, sending the target risk screening data and the target basic information data to the decision engine includes: matching each field information of the target risk screening data and the target basic information data with the first target field information one by one through JAVA reflection, wherein the first target field information is the field information of the data on which the decision engine executes the decision based.

[0054] According to an embodiment of the present disclosure, the above-mentioned data processing device also includes a parsing module and a storage module.

[0055] The parsing module is used to parse the original result data related to the decision result to obtain the decision result data; the storage module is used to store the decision result data in the database of the data processing system.

[0056] According to an embodiment of the present disclosure, in the above-mentioned storage module, storing the decision result data in the database of the data processing system includes: matching each field information of the decision result data with the second target field information one by one through JAVA reflection, wherein the second target field information is the field information used when the database stores the decision result data.

[0057] According to an embodiment of the present disclosure, the above-mentioned data processing device also includes: a reading module, which is used to read the decision result from the database according to a preset time interval, so as to return the decision result to the installment service request end.

[0058] Another aspect of the present disclosure provides an electronic device, comprising: one or more processors, and a memory; wherein the memory is used to store one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the data processing method as described above.

[0059] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the data processing method described above when executed.

[0060] Another aspect of the present disclosure provides a computer program product, which includes computer executable instructions, and when the instructions are executed, are used to implement the data processing method as described above.

[0061] According to the embodiments of the present disclosure, through the above-mentioned data processing method, a set of general data processing processes that can be dynamically configured and adaptively supported for different installment products are established. Through this method, the common approval stages of various installment business lines, including the verification of basic information data and risk screening of customers to obtain risk screening data, are integrated into the same set of data processing processes. Different installment business lines can share the same set of data processing methods. The approval process can be executed simply by entering different customer information data on the product side, and a pluggable processing mode is supported. For newly added / deleted installment business lines, the business scenario switching can be completed by configuring the basic information data. It has high flexibility and strong scalability, and can dynamically adapt to changes in business rules, improve development efficiency, and reduce development risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0063] Figure 1 An exemplary system architecture to which the data processing method and apparatus of the present disclosure can be applied is schematically shown;

[0064] Figure 2 A flowchart schematically shows a data processing method according to an embodiment of the present disclosure;

[0065] Figure 3 A flowchart schematically shows how to verify original request data according to preset verification rules according to an embodiment of the present disclosure;

[0066] Figure 4 A schematic diagram schematically shows a method of verifying original request data according to a preset verification rule according to an embodiment of the present disclosure;

[0067] Figure 5 A flowchart for obtaining target risk screening data according to an embodiment of the present disclosure is schematically shown;

[0068] Figure 6 A schematic diagram of obtaining target risk screening data according to an embodiment of the present disclosure is schematically shown;

[0069] Figure 7 A schematic diagram schematically shows sending target risk screening data and target basic information data to a decision engine and receiving data from the decision engine according to an embodiment of the present disclosure;

[0070] Figure 8 A schematic diagram schematically shows a data processing method according to an embodiment of the present disclosure;

[0071] Fig. 9A schematic diagram schematically shows a method of reading a decision result from a database at a preset time interval and regularly pushing the decision result to an installment service request terminal according to an embodiment of the present disclosure;

[0072] Fig.10 A block diagram schematically shows a data processing device according to an embodiment of the present disclosure; and

[0073] Fig.11 A block diagram of an electronic device for implementing a data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0074] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0075] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0076] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0077] In the case of using expressions such as "at least one of A, B, and C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.). In the case of using expressions such as "at least one of A, B, or C, etc.", it should generally be interpreted in accordance with the meaning of the expression generally understood by those skilled in the art (for example, "a system having at least one of A, B, or C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0078] Before elaborating on the embodiments of the present disclosure in detail, the system structure and application scenarios involved in the methods provided in the embodiments of the present disclosure are first introduced as follows.

[0079] Figure 1 The exemplary system architecture 100 to which the data processing method and apparatus of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0080] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0081] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients and / or social platform software, etc. (only for example).

[0082] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0083] The server 105 may be a server that provides various services, such as a background management server that provides support for websites browsed by users using terminal devices 101, 102, and 103. (The server 105 may be a server that provides various services, including but not limited to service one, service two, service three, service four, etc. Service one, service two, service three, and service four may be, for example, services that provide support for websites browsed by users using terminal devices 101, 102, and 103.) The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0084] Special installment business refers to the personal mortgage credit loan business provided by financial institutions based on the specific consumption needs of customers and the comprehensive customer qualifications. Under this system architecture, users can use terminal devices 101, 102, and 103 to send original request data to server 105 through network 104. The original request data may include various customer information data and business information data. Server 105 can be used to process the original request data and then send the processed data to the downstream services of the system, such as the decision engine, so that the decision engine can perform decision tasks based on these data, screen the risks of customers and generate decision results.

[0085] According to an embodiment of the present disclosure, the server 105 performs data processing on the original request data, which may be to perform data verification on the original request data to obtain data that conforms to a specific data type; it may also be to perform data interaction with an external componentized service based on the original request data to obtain some intermediate result data, such as risk screening data, etc.

[0086] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0087] It should be noted that the data processing method and device disclosed in the present invention can be used in the field of big data technology, in the field of financial technology, or in any field other than the field of big data technology and the field of finance. The present disclosure does not limit the application field of the data processing method and device.

[0088] With the rapid development of the Internet financial industry today, more and more installment demands are coming one after another, including cash installment, E installment, car installment, home improvement installment, etc. Special installment business refers to the personal mortgage credit loan business provided by financial institutions based on the specific consumption needs of customers and the comprehensive customer qualifications. After the customer submits the installment application, the financial institution needs to review it and use the big data rule engine system, such as the Blaze decision engine, to screen the risk of customers and make decisions based on various relevant information sent in the approval process.

[0089] In the related art, different installment business lines need to be approved separately, and different installment products need to request data interactively separately, which results in poor maintainability and scalability.

[0090] In the process of implementing the present disclosure, it is found that the different approval processes of various installment products are generally similar. Almost all installment business lines need to verify basic information data and screen customers' risks during the approval process. The difference lies mainly in the different customer information entered from the product end and the different individual information sent to the Blaze decision engine. Therefore, by establishing a common data processing system, different installment business lines can share the same data processing method to speed up the approval process and improve the maintainability and scalability of the approval system. Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0091] like Figure 2 As shown, the method includes operations S201 to S204.

[0092] In operation S201, original request data from the installment service request end is received, and the original request data includes original customer information data and original service information data, wherein the original service information data is related to the service type of the installment product. The original customer information data mainly includes some basic personal information data of the customer, such as name, age, ID number, monthly income, etc. The original service information data is related to the service type of the installment product. For example, for automobile installment products, the original service information data may include collateral information, etc., and for home improvement installment products, the original service information data may include real estate qualification information, etc.

[0093] In operation S202, the original request data is verified according to a preset verification rule, so as to determine the original request data that passes the verification as the target basic information data, wherein the target basic information data is data that complies with a preset data format and a preset data type.

[0094] In operation S203, target risk screening data is obtained, wherein the target risk screening data is risk screening data that matches the target risk screening data type, and the target risk screening data type is the risk screening data type on which the decision engine performs a decision.

[0095] In operation S204, the target risk screening data and the target basic information data are sent to the decision engine, so that the decision engine performs data processing according to the target risk screening data and the target basic information data, and returns the original result data related to the decision result to the data processing system. The decision engine is a downstream service system of the data processing system, which is used to perform risk screening and decision-making on customers according to various relevant information sent in the approval process, for example, the Blaze decision engine can be used.

[0096] Because in this data processing system, the main thing is to pre-process the original request data of the request end and obtain some intermediate result data, and then send these data to the downstream services of the system, such as the decision engine, so that the decision engine can perform decision tasks based on these data, perform risk screening on customers and generate decision results. In the process of data interaction with the decision engine, the data sent to the decision engine needs to comply with the data type standard provided by the decision engine. Therefore, the preset data type of the target basic information data is the request data type based on which the decision engine executes the decision; the target risk screening data type of the target risk screening data is the risk screening data type based on which the decision engine executes the decision, for example, it may include blacklist risk screening result data, anti-fraud risk screening result data, cross-default risk screening result data, etc. The target risk screening data type required may be different under different installment product business types.

[0097] In addition, after the target basic information data is generated, the data needs to be persisted and stored in the local database for subsequent processing flow calls. Therefore, the preset data format of the target basic information data is the data format that needs to be satisfied when the data is stored in the database of the data processing system, such as the length of the data, the format of the data dictionary, the format of the date, etc.

[0098] The preset verification rules are customized by the user. After the original request data is verified by the verification rules, its purpose is to be able to judge and filter the data sent on the installment business request end to obtain the target basic information data that meets the preset data standards.

[0099] According to the embodiments of the present disclosure, through the above-mentioned data processing method, a set of general data processing processes that can be dynamically configured and adaptively supported for different installment products are established. Through this method, the common approval stages of various installment business lines, including the verification of basic information data and risk screening of customers to obtain risk screening data, are integrated into the same set of data processing processes. Different installment business lines can share the same set of data processing methods. The approval process can be executed simply by entering different customer information data on the product side, and a pluggable processing mode is supported. For newly added / deleted installment business lines, the business scenario switching can be completed by configuring the basic information data. It has high flexibility and strong scalability, and can dynamically adapt to changes in business rules, improve development efficiency, and reduce development risks.

[0100] It should be noted that the acquisition, storage and application of user personal information involved in the embodiments of the present disclosure are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0101] According to an embodiment of the present disclosure, the preset verification rules include a first preset verification rule and a second preset verification rule, wherein the first preset verification rule is used to determine whether the data type of the original request data conforms to the preset data type, and the second preset verification rule is used to determine whether the data format of the original request data conforms to the preset data format, wherein the preset data type is the request data type on which the decision engine executes the decision, and the preset data format is the data format that needs to be satisfied when data is stored in the database of the data processing system.

[0102] According to an embodiment of the present disclosure, further, the first preset verification rule includes a general customer information verification rule and a plurality of unique business information verification rules, wherein the general customer information verification rule is used for data type verification of original customer information data under various installment product business types, and the plurality of unique business information verification rules are respectively used for data type verification of original business information data under each installment product business type.

[0103] According to an embodiment of the present disclosure, further, the preset data type includes a first preset data type for characterizing the target data type of original customer information data under various installment product business types, and a second preset data type for characterizing the target data type of original business information data under each installment product business type, wherein the first preset data type is the customer information data type on which the decision engine executes a decision; and wherein the second preset data type is the business information data type on which the decision engine executes a decision.

[0104] Figure 3 A flowchart schematically shows how to verify original request data according to preset verification rules according to an embodiment of the present disclosure; Figure 4 The schematic diagram schematically shows a schematic diagram of verifying the original request data according to the preset verification rules according to an embodiment of the present disclosure. Figure 3 , Figure 4 Based on the above verification rules, the method of verifying the original request data according to the preset verification rules in the embodiment of the present disclosure is described as follows:

[0105] like Figure 3 As shown, the method includes operations S301 to S303.

[0106] After receiving the original request data from the installment business request end, first determine whether the installment product is a valid installment product based on the original request data, such as customer information and basic product business information, such as whether the installment product is online. If invalid, terminate the processing flow to avoid subsequent approval processes when the installment product has been removed from the shelves, causing unnecessary waste of time.

[0107] When the installment product is valid, in operation S301, the data type of the customer information data under various installment product business types is checked according to the general customer information verification rules to see if it conforms to the first preset data type. According to an embodiment of the present disclosure, for different installment products, the type of customer information data on which the decision engine executes decisions is basically the same. Therefore, in order to speed up the data processing process, the customer information verification operation can be extracted into a general processing flow. The first preset data type is determined according to the data interface standard provided by the decision engine end, and may include, for example, name, age, ID number, monthly income, etc. The purpose of this operation is to verify whether the data type of the customer information data is complete so as to serve as the basis for the decision engine to execute decisions.

[0108] In operation S302, the data type of the original business information data under each installment product business type is checked according to each unique business information verification rule to see whether it conforms to the second preset data type. For each installment product, the data type of the original business information data is related to the business type of each product. For example, for automobile installment products, the original business information data must include collateral information, and for home improvement installment products, the original business information data must include real estate qualification information. For the verification of this part of data, it is necessary to execute the unique business information verification rules to perform the verification.

[0109] In operation S303, it is verified according to a second preset verification rule whether the data formats of the customer information data and the original business information data conform to the preset data format.

[0110] According to an embodiment of the present disclosure, after executing the above-mentioned data verification process, the original request data that meets the preset data type and preset data format is determined as the target basic information data. After the target basic information data is generated, the data needs to be persisted and stored in the local database for subsequent processing flow calls. Therefore, the preset data format of the target basic information data needs to meet the database storage data format standard, such as whether the length of the data meets the set length standard, whether the format of the data dictionary is consistent with the preset dictionary format, whether the date format is the preset date format, etc. After executing the above-mentioned data verification process, the target basic information data is stored in the basic information data table of the local database, and the subsequent process is executed after the verification is successful; otherwise, an error message is prompted and returned.

[0111] According to the embodiments of the present disclosure, by setting preset verification rules, and further setting the preset verification rules to include a first preset verification rule including a general customer information verification rule and a plurality of unique business information verification rules, and a second preset verification rule. Through the above verification rules, the common verification process of different installment business line products can be extracted for unified verification. For the unique inspection process of each installment business line product, the respective unique business information verification rules are executed for verification, without having to execute each independent and complete verification process for each installment business product. Therefore, through the above verification method, the efficiency of data processing can be improved and the approval process can be accelerated.

[0112] According to an embodiment of the present disclosure, verifying the original request data according to preset verification rules includes: adding custom annotations to the original request data; and verifying each data in the original request data separately by using custom annotations and preset verification rules through JAVA reflection.

[0113] In the related art, when verifying the original request data, each request field is hard-coded separately. Due to the wide variety of products in the installment business line and the huge number of verification data items, a large amount of redundant code will be generated, and the maintainability and scalability are poor.

[0114] In order to solve the above technical problems, according to the embodiments of the present invention, by adding custom annotations to the original request data, that is, adding corresponding annotations to the field positions where the product-side input information needs to be verified, annotating the data format, data type, etc. of the field, and combining the use of JAVA reflection, according to preset verification rules, universal verification of installment business information can be achieved, including data must be entered verification, upload stage verification, length, dictionary, date, etc. Verification, eliminating traditional data verification and hard-coding of upload parameters, with a high code reuse rate, reducing redundant code and reducing manual operations.

[0115] Figure 5 A flowchart for obtaining target risk screening data according to an embodiment of the present disclosure is schematically shown; Figure 6 The following schematic diagram shows a schematic diagram of obtaining target risk screening data according to an embodiment of the present disclosure. Figure 5 , Figure 6 A method for obtaining target risk screening data according to an embodiment of the present disclosure is described.

[0116] like Figure 5 As shown, the method includes operations S501 to S504.

[0117] In operation S501, the target basic information data is parsed to obtain the installment product business type; for example, the installment product business type can be determined by parsing the business information data related to the installment product business type in the target basic information data.

[0118] In operation S502, basic information data on risk screening related to the installment product business type is obtained.

[0119] Before executing the data processing method of the embodiment of the present disclosure, the parameter configuration table of the installment business process can be configured in advance. The parameter configuration table mainly records the risk screening basic information data required for the approval process of executing different types of installment product businesses. The risk screening basic information data is used to characterize the types of some basic information data required for executing various types of risk screening. For example, when executing blacklist risk screening, the customer's name, home address, telephone number, historical credit record information, etc. are required. The above-mentioned risk screening basic information data can be obtained by reading the parameter configuration table of the installment business process.

[0120] According to an embodiment of the present disclosure, before executing the operation, the parameter configuration table of the installment business process can be read to obtain some customer information and basic product business information to determine whether the installment product business line is valid. If invalid, the processing flow is terminated.

[0121] In operation S503, if the installment product business line is valid, basic risk screening data is obtained based on the risk screening basic information data, wherein the basic risk screening data is all types of risk screening data that match the business type of the installment product, and the basic risk screening number and the target risk screening data are both result data of risk screening, for example, the result data of whether the customer has fraudulent behavior, and the result data of whether the customer has cross-default lines. Specifically, the above-mentioned risk screening basic information data related to the installment product business type can be transmitted to the external risk screening application by calling the interface connected to the external risk screening application, and the basic risk screening data can be obtained by data interaction with the external risk screening application, and after obtaining these data, this part of the data is stored in the risk screening data table of the local database.

[0122] In operation S504, data matching the target risk screening data type is screened from the basic risk screening data to obtain the target risk screening data. According to an embodiment of the present disclosure, the data node of the target risk screening data type to be sent can be registered in advance by means of custom annotations, and then the risk screening data to be sent to the decision engine can be configured to the data node of the corresponding target risk screening data type, that is, the required risk screening data is injected into the data node under different installment business types. When executing this operation process, firstly, according to the determined installment product business type, the type of the target risk screening data under the installment product business type is judged, and then the risk screening data result under the type of the corresponding risk screening data is read from the risk screening data table of the local database according to the type of the target risk screening data. For example, when performing the approval of automobile installment products, the types of the target risk screening data under the installment product business type include anti-fraud risk screening data and blacklist risk screening data. Afterwards, the screening results of the anti-fraud and blacklist risk screening of the customer are read from the risk screening data table of the local database.

[0123] According to an embodiment of the present disclosure, sending the target risk screening data and the target basic information data to the decision engine includes: matching each field information of the target risk screening data and the target basic information data with the first target field information one by one through JAVA reflection, wherein the first target field information is the field information of the data on which the decision engine executes the decision based on.

[0124] Figure 7 The schematic diagram schematically shows sending target risk screening data and target basic information data to a decision engine and receiving data from the decision engine according to an embodiment of the present disclosure.

[0125] In the related technology, when sending the target risk screening data and the target basic information data to the decision engine, each uploaded field is hard-coded separately. Due to the wide variety of products in the installment business line and the huge number of inspection data items, a large amount of redundant code will be generated.

[0126] In order to solve the above technical problems, according to the embodiments of the present disclosure, each field information of the target risk screening data and the target basic information data is matched one by one with the first target field information by JAVA reflection, such as Figure 7 As shown, the implementation method is to use JAVA reflection technology to traverse and match the target risk screening data and the target basic information data one by one until all field information is successfully matched. During execution, it can be processed according to the actual situation. For example, when the data to be matched is unnecessary information data when the decision engine executes the decision, the matching failure can be ignored. Otherwise, an exception is returned if the matching failure occurs.

[0127] According to the embodiments of the present disclosure, each field information of the target risk screening data and the target basic information data is matched one by one with the first target field information by means of JAVA reflection, so that the acquired data can be uniformly and automatically mapped into the data that needs to be sent to the decision engine, eliminating the traditional data verification and hard-coding method of sending parameters, with a high code reuse rate, reducing redundant code and reducing manual operations.

[0128] According to the embodiment of the present disclosure, after the target risk screening data and the target basic information data are sent to the decision engine, the request data can be converted into an XML message format through the JAXB Context (Java Architecture for XML Binding API entry) of JDK (a development environment for building applications, applets, and components using the Java programming language). Create a local client, and complete the call of the decision engine through the generated XML message in the form of a web service. After calling the decision engine to execute the decision, receive the original result data related to the decision result returned by the decision engine.

[0129] According to an embodiment of the present disclosure, the data processing method further includes: parsing original result data related to the decision result to obtain decision result data; and storing the decision result data in a database of the data processing system.

[0130] According to an embodiment of the present disclosure, the data processing method further includes: reading the decision result from the database at a preset time interval so as to return the decision result to the installment service request end.

[0131] Figure 8 The following is a schematic diagram of a data processing method according to an embodiment of the present disclosure. Figure 8 As shown, the processing method may include the following process:

[0132] (1) Receiving original request data from the installment business request end; the data sent by the installment business request end may be, for example, the customer's basic personal information data, such as name, age, ID number, monthly income, etc., and also includes business information data related to different installment product business types, such as mortgage information, real estate qualification information, etc.

[0133] (2) Perform a general check on the original request data according to the preset check rules, so as to determine the original request data that has passed the check as the target basic information data. Specifically, the preset check rules are used to check whether the data type and data format of the original request data meet the preset standards, that is, whether they meet the data type standards based on which the decision engine executes decisions, and whether they meet the standards for storing data formats in the database, such as whether the length of the data meets the set length standards, whether the format of the data dictionary matches the preset dictionary format, whether the date format is the preset date format, etc. During the check process, the interface processor can perform related data reading operations. For example, when checking whether the data format meets the preset format, the interface processor can read the dictionary items in the database, and then compare and check them one by one with the data sent by the request end. After the check passes, the target basic information data can be stored in the basic information data table in the local database.

[0134] (3) Obtain target risk screening data. First, by reading the pre-configured parameter configuration table of the installment business process, obtain the type of risk screening basic information data used to characterize some basic information data required to perform various types of risk screening. After that, the interface processor can call the interface that connects to the external risk screening application to transmit the above-mentioned risk screening basic information data related to the installment product business type to the external risk screening application, and obtain the basic risk screening data through data interaction with the external risk screening application. After obtaining this data, this part of the data is stored in the risk screening data table of the local database. Finally, the data that matches the target risk screening data type can be filtered from the basic risk screening data to obtain the target risk screening data.

[0135] (4) Send the target risk screening data and target basic information data to the Blaze decision engine.

[0136] (5) The Blaze decision engine performs data processing based on the target risk screening data and target basic information data, and returns the original result data related to the decision result to the data processing system.

[0137] (6) Parse the original result data returned by the Blaze decision engine to obtain the decision result data.

[0138] (7) Store the decision result data in the database of the data processing system.

[0139] (8) Read the decision results from the database at preset time intervals and push the decision results to the installment business request end on a regular basis. Fig. 9 The schematic diagram schematically shows a method of reading decision results from a database at a preset time interval and regularly pushing the decision results to the installment service request terminal according to an embodiment of the present disclosure. Fig. 9As shown in the figure, the push execution thread number and execution time of the operation are configured through the custom thread pool, the data stored in the database of the data processing system is scanned regularly, the decision results are queried, and the returned conclusions are pushed regularly according to the interface rules and policies provided by the product side. If the push fails, the retry is performed according to the rules agreed by the upstream and downstream. If the push is successful, all the installment business processes are closed.

[0140] According to an embodiment of the present disclosure, storing decision result data in a database of a data processing system includes: matching each field information of the decision result data with the second target field information one by one through JAVA reflection, wherein the second target field information is the field information used when the database stores the decision result data.

[0141] According to the embodiments of the present disclosure, each field information of the decision result data is matched one by one with the second target field information by means of JAVA reflection, so that the acquired data can be uniformly and automatically mapped into the data that needs to be stored in the database, eliminating the traditional data verification and hard-coding method of uploading parameters, with a high code reuse rate, reducing redundant code and reducing manual operations.

[0142] Fig.10 A block diagram 1000 of a data processing device according to an embodiment of the present disclosure is schematically shown. The device 1000 can be used to implement reference Figure 2 The method shown.

[0143] like Fig.10 As shown, the data processing device 1000 includes: a receiving module 1001 , a checking module 1002 , an acquiring module 1003 and a sending module 1004 .

[0144] The receiving module 1001 is used to receive original request data from the installment service request end, wherein the original request data includes original customer information data and original service information data, wherein the original service information data is related to the installment product service type.

[0145] The verification module 1002 is used to verify the original request data according to the preset verification rules, so as to determine the original request data that passes the verification as the target basic information data, wherein the target basic information data is data that conforms to the preset data format and the preset data type.

[0146] The acquisition module 1003 is used to acquire target risk screening data, wherein the target risk screening data is risk screening data that matches the target risk screening data type, and the target risk screening data type is the risk screening data type on which the decision engine executes decisions.

[0147] The sending module 1004 is used to send the target risk screening data and the target basic information data to the decision engine, so that the decision engine performs data processing based on the target risk screening data and the target basic information data, and then returns the original result data related to the decision result to the data processing system.

[0148] According to the embodiments of the present disclosure, a set of general data processing processes that can be dynamically configured and adaptively supported for different installment products can be established through the verification module 1002 and the acquisition module 1003 of the above-mentioned data processing device. Through this device, the common approval stages of various installment business lines, including the verification of basic information data and the risk screening of customers to obtain risk screening data, are integrated into the same set of data processing processes. Different installment business lines can share the same set of data processing processes. The approval process can be executed simply by entering different customer information data on the product side, and a pluggable processing mode is supported. For newly added / deleted installment business lines, the switching of business scenarios can be completed only by configuring the basic information data. It has high flexibility and strong scalability, and can dynamically adapt to changes in business rules, improve development efficiency, and reduce development risks.

[0149] According to an embodiment of the present disclosure, the preset verification rules include a first preset verification rule and a second preset verification rule, wherein the first preset verification rule is used to determine whether the data type of the original request data conforms to the preset data type, and the second preset verification rule is used to determine whether the data format of the original request data conforms to the preset data format, wherein the preset data type is the request data type on which the decision engine executes the decision, and the preset data format is the data format that needs to be satisfied when data is stored in the database of the data processing system.

[0150] According to an embodiment of the present disclosure, the first preset verification rule includes a general customer information verification rule and multiple unique business information verification rules, wherein the general customer information verification rule is used for data type verification of original customer information data under various installment product business types, and the multiple unique business information verification rules are respectively used for data type verification of original business information data under each installment product business type.

[0151] According to an embodiment of the present disclosure, the preset data type includes a first preset data type for representing a target data type of original customer information data under various installment product business types, and a second preset data type for representing a target data type of original business information data under each installment product business type, wherein the first preset data type is a customer information data type based on which the decision engine performs a decision; wherein the second preset data type is a business information data type based on which the decision engine performs a decision;

[0152] The verification module 1002 includes a first verification unit, a second verification unit and a third verification unit.

[0153] Among them, the first verification unit is used to verify whether the data type of customer information data under various installment product business types conforms to the first preset data type according to the general customer information verification rules; the second verification unit is used to verify whether the data type of the original business information data under each installment product business type conforms to the second preset data type according to each unique business information verification rule; the third verification unit is used to verify whether the data format of the customer information data and the original business information data conforms to the preset data format according to the second preset verification rule.

[0154] According to an embodiment of the present disclosure, the verification module 1002 includes an adding unit and a fourth verification unit.

[0155] Among them, the adding unit is used to add custom annotations to the original request data; the fourth verification unit is used to verify each data in the original request data separately by using JAVA reflection, custom annotations and preset verification rules.

[0156] According to an embodiment of the present disclosure, the acquisition module 1003 includes an acquisition unit and a screening unit.

[0157] The acquisition unit is used to acquire basic risk screening data based on the target basic information data, wherein the basic risk screening data is all types of risk screening data that match the business type of the installment product; the screening unit is used to screen data that matches the target risk screening data type from the basic risk screening data to obtain the target risk screening data.

[0158] According to an embodiment of the present disclosure, the acquisition unit includes a parsing subunit, a first acquisition subunit, and a second acquisition subunit.

[0159] Among them, the parsing subunit is used to parse the target basic information data to obtain the installment product business type; the first acquisition subunit is used to obtain the risk screening basic information data related to the installment product business type; the second acquisition subunit is used to obtain basic risk screening data based on the risk screening basic information data.

[0160] According to an embodiment of the present disclosure, in the above-mentioned sending module 1004, sending the target risk screening data and the target basic information data to the decision engine includes: matching each field information of the target risk screening data and the target basic information data with the first target field information one by one through JAVA reflection, wherein the first target field information is the field information of the data on which the decision engine executes the decision based.

[0161] According to an embodiment of the present disclosure, the above-mentioned data processing device also includes a parsing module and a storage module.

[0162] The parsing module is used to parse the original result data related to the decision result to obtain the decision result data; the storage module is used to store the decision result data in the database of the data processing system.

[0163] According to an embodiment of the present disclosure, in the above-mentioned storage module, storing the decision result data in the database of the data processing system includes: matching each field information of the decision result data with the second target field information one by one through JAVA reflection, wherein the second target field information is the field information used when the database stores the decision result data.

[0164] According to an embodiment of the present disclosure, the above-mentioned data processing device also includes: a reading module, which is used to read the decision result from the database according to a preset time interval, so as to return the decision result to the installment service request end.

[0165] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0166] For example, any multiple of the receiving module 1001, the verification module 1002, the acquisition module 1003, and the sending module 1004 can be combined in one module / unit / sub-unit for implementation, or any one of the modules / units / sub-units can be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to an embodiment of the present disclosure, at least one of the receiving module 1001, the verification module 1002, the acquisition module 1003, and the sending module 1004 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware, and firmware, or by a suitable combination of any of them. Alternatively, at least one of the receiving module 1001, the checking module 1002, the acquiring module 1003 and the sending module 1004 may be at least partially implemented as a computer program module, and when the computer program module is executed, a corresponding function may be executed.

[0167] Fig.11 A block diagram of an electronic device for implementing a data processing method according to an embodiment of the present disclosure is schematically shown. Fig.11 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0168] like Fig.11 As shown, the electronic device 1100 according to an embodiment of the present disclosure includes a processor 1101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1102 or a program loaded from a storage part 1108 to a random access memory (RAM) 1103. The processor 1101 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1101 may also include an onboard memory for caching purposes. The processor 1101 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0169] In RAM 1103, various programs and data required for the operation of electronic device 1100 are stored. Processor 1101, ROM 1102 and RAM 1103 are connected to each other through bus 1104. Processor 1101 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 1102 and / or RAM 1103. It should be noted that the program can also be stored in one or more memories other than ROM 1102 and RAM 1103. Processor 1101 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0170] According to an embodiment of the present disclosure, the electronic device 1100 may further include an input / output (I / O) interface 1105, which is also connected to the bus 1104. The electronic device 1100 may further include one or more of the following components connected to the I / O interface 1105: an input portion 1106 including a keyboard, a mouse, etc.; an output portion 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1108 including a hard disk, etc.; and a communication portion 1109 including a network interface card such as a LAN card, a modem, etc. The communication portion 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the I / O interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as needed, so that a computer program read therefrom is installed into the storage portion 1108 as needed.

[0171] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1109, and / or installed from the removable medium 1111. When the computer program is executed by the processor 1101, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0172] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0173] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: 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), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0174] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 1102 and / or the RAM 1103 described above and / or one or more memories other than the ROM 1102 and the RAM 1103 .

[0175] The embodiments of the present disclosure also include a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiments of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the data processing method provided by the embodiments of the present disclosure.

[0176] When the computer program is executed by the processor 1101, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0177] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1109, and / or installed from a removable medium 1111. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0178] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0179] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments and / or claims of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0180] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method, comprising: Receiving original request data from an installment service request terminal, the original request data including original customer information data and original service information data, wherein the original service information data is related to an installment product service type; The original request data is verified according to preset verification rules, so as to determine the original request data that meets the preset data standard as the target basic information data, wherein the preset verification rules include a first preset verification rule, the first preset verification rule is used to determine whether the data type of the original request data meets the preset data type, the preset data type is the request data type based on which the decision engine executes the decision, the first preset verification rule includes a general customer information verification rule and a plurality of unique business information verification rules, wherein the general customer information verification rule is used for data type verification of the original customer information data under various installment product business types, and the plurality of unique business information verification rules are respectively used for data type verification of the original business information data under respective installment product business types, and the target basic information data is data that meets the preset data format and preset data type; Acquire target risk screening data, wherein the target risk screening data is risk screening data that matches a target risk screening data type, and the target risk screening data type is a risk screening data type on which a decision engine performs a decision; and The target risk screening data and the target basic information data are sent to the decision engine so that the decision engine performs data processing according to the target risk screening data and the target basic information data, and then returns the original result data related to the decision result to the data processing system.

2. The method according to claim 1, wherein: The preset verification rules also include a second preset verification rule, wherein the second preset verification rule is used to determine whether the data format of the original request data conforms to the preset data format, wherein the preset data format is a data format that needs to be satisfied when data is stored in the database of the data processing system.

3. The method according to claim 1, wherein: The preset data types include a first preset data type for representing the target data type of the original customer information data under various installment product business types, and a second preset data type for representing the target data type of the original business information data under each installment product business type, wherein the first preset data type is the customer information data type based on which the decision engine performs decisions; The second preset data type is the business information data type on which the decision engine performs decisions; The verifying the original request data according to the preset verification rule includes: Verifying, according to the general customer information verification rule, whether the data type of the customer information data under various installment product business types conforms to the first preset data type; Verifying, according to each of the unique business information verification rules, whether the data type of the original business information data under each installment product business type conforms to the second preset data type; The second preset verification rule is used to verify whether the data formats of the customer information data and the original business information data conform to the preset data format.

4. According to the method of claim 1, verifying the original request data according to a preset verification rule comprises: Adding custom annotations to the original request data; By means of JAVA reflection, each data in the original request data is verified respectively using the custom annotation and the preset verification rules.

5. According to the method of claim 1, obtaining target risk screening data comprises: Acquire basic risk screening data according to the target basic information data, wherein the basic risk screening data is all types of risk screening data matching the business type of the installment product; Data matching the target risk screening data type is screened from the basic risk screening data to obtain the target risk screening data.

6. The method according to claim 5, wherein: Obtaining basic risk screening data based on the target basic information data includes: Parsing the target basic information data to obtain the installment product business type; Obtaining basic risk screening information data related to the business type of the installment product; The basic risk screening data is obtained according to the risk screening basic information data.

7. According to the method of claim 1, sending the target risk screening data and the target basic information data to the decision engine comprises: By means of JAVA reflection, each field information of the target risk screening data and the target basic information data is matched one by one with the first target field information, wherein the first target field information is the field information of the data on which the decision engine executes the decision.

8. The method according to claim 1, further comprising: Parsing the original result data related to the decision result to obtain the decision result data; The decision result data is stored in a database of a data processing system.

9. The method according to claim 8, storing the decision result data in a database of a data processing system comprises: By means of JAVA reflection, each field information of the decision result data is matched one by one with the second target field information, wherein the second target field information is the field information used when the database stores the decision result data.

10. The method according to claim 8, further comprising: The decision result is read from the database at a preset time interval so as to return the decision result to the installment service requesting end.

11. A data processing device, comprising: A receiving module, configured to receive original request data from an installment service request terminal, wherein the original request data includes original customer information data and original service information data, wherein the original service information data is related to the installment product service type; A verification module, used to verify the original request data according to preset verification rules, so as to determine the original request data that meets the preset data standard as the target basic information data, wherein the preset verification rules include a first preset verification rule, the first preset verification rule is used to determine whether the data type of the original request data meets the preset data type, the preset data type is the request data type based on which the decision engine executes the decision, the first preset verification rule includes a general customer information verification rule and a plurality of unique business information verification rules, wherein the general customer information verification rule is used for data type verification of the original customer information data under various installment product business types, and the plurality of unique business information verification rules are respectively used for data type verification of the original business information data under respective installment product business types, and the target basic information data is data that meets the preset data format and preset data type; An acquisition module, used for acquiring target risk screening data, wherein the target risk screening data is risk screening data matching the target risk screening data type, and the target risk screening data type is the risk screening data type on which the decision engine performs a decision; A sending module is used to send the target risk screening data and the target basic information data to the decision engine, so that the decision engine performs data processing based on the target risk screening data and the target basic information data, and then returns the original result data related to the decision result to the data processing system.

12. An electronic device comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the method according to any one of claims 1 to 10.

14. A computer program product, comprising computer executable instructions, which when executed are used to implement the method of any one of claims 1 to 10.

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