Business data processing method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a service data processing method and device, equipment and a storage medium, and the method comprises the steps: receiving service request information sent by terminal equipment, the service request information comprises a user identifier, according to the user identifier, determining service data, the service data comprises first service data and second service data, and according to the user identifier, sending the first service data to the terminal equipment, and determining a user type of the user, determining first prediction data according to the user type and the first service data, determining second prediction data according to the user identifier and the second service data, determining target service data of the user according to the first prediction data and the second prediction data, and sending the target service data to the terminal equipment. According to the method, the business handling efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of data processing, and specifically relates to a business data processing method, device, equipment and storage medium. Background Art

[0002] When users apply for target business, they need to provide relevant data for proof. For example, when users apply for loan amount, they need to provide relevant financial proof so that the review center can determine the specific loan amount.

[0003] At present, the process of users handling target business is as follows: users provide corresponding data according to the system prompts, wait for manual review, and obtain the target business data after manual review. If the user believes that the target business data does not meet the requirements, additional data needs to be supplemented and then wait for manual review.

[0004] In the above situation, the processing procedure of the target business is relatively long, resulting in low business processing efficiency. Summary of the invention

[0005] The embodiments of the present application relate to a business data processing method, device, equipment and storage medium, which are used to solve the defect in the prior art that the processing procedure of the target business is long, resulting in low business processing efficiency.

[0006] In a first aspect, an embodiment of the present application provides a business data processing method, which is applied to a main server, and the method includes:

[0007] Receiving service request information sent by a terminal device, wherein the service request information includes a user identifier;

[0008] Determine service data according to the user identifier, the service data including first service data and second service data;

[0009] Determining the user type of the user according to the user identifier;

[0010] Determining first prediction data according to the user type and the first service data;

[0011] Determining second prediction data according to the user identifier and the second service data;

[0012] The target service data of the user is determined according to the first prediction data and the second prediction data, and the target service data is sent to the terminal device.

[0013] In a possible implementation manner, determining first prediction data according to the user type and the first service data includes:

[0014] Determining whether the user type is the first type;

[0015] If yes, determining first feature data and second feature data according to the first business data, and determining the sum of the first feature data and the second feature data as first prediction data;

[0016] If not, first characteristic data is determined according to the first business data, and the first characteristic data is determined as the first prediction data.

[0017] In a possible implementation manner, the first business data includes income data, income-related data, company data, and company-related data, and determining the first feature data and the second feature data according to the first business data includes:

[0018] Inputting the income-related data into a data processing model to obtain processed data corresponding to the data processing model;

[0019] When the difference between the processed data and the income data is within a preset range, determining the income data as the first feature data;

[0020] Determine the target proportion of users according to the company data;

[0021] The second characteristic data is determined according to the company-related data and the target proportion.

[0022] In a possible implementation manner, the second business data includes non-revolving loan data, and determining the second prediction data according to the user identifier and the second business data includes:

[0023] Generate a data query request according to the user identifier, and send the data query request to the first server;

[0024] receiving a data query response sent by the first server, wherein the data query response includes query data;

[0025] According to the query data, the non-revolving loan data is verified to obtain a verification result, wherein the verification result includes a verification success and a verification failure;

[0026] When the verification result is successful, the non-revolving loan data is input into a data prediction model to obtain a predicted value of the data prediction model, and the predicted value is determined as the second predicted data.

[0027] In a possible implementation manner, determining the target service data of the user according to the first prediction data and the second prediction data includes:

[0028] Determine the maximum value between the first prediction data and the second prediction data as target prediction data;

[0029] determining a target value according to the second business data;

[0030] Acquire at least one data field corresponding to the first business data;

[0031] The target business data of the user is determined according to the target prediction data, the target value and the at least one data field.

[0032] In a possible implementation manner, the second business data includes non-revolving loan data and revolving loan data, and determining the target value according to the second business data includes:

[0033] Determining a first average value corresponding to a first period based on the non-revolving loan data;

[0034] Determining a second average value corresponding to the first period based on the revolving loan data;

[0035] Determine the product of the second average value and the first preset value as a first value;

[0036] Determine a second value according to the revolving loan data and a second preset value;

[0037] A sum of the first average value, the first value, and the second value is determined as a target value.

[0038] In a possible implementation, determining the target service data of the user according to the target prediction data, the target value and the at least one data field includes:

[0039] Determining first business data according to the target prediction data and the target value;

[0040] Performing identification processing on the at least one data field to determine whether a target field exists;

[0041] If yes, when the field value corresponding to the target field is the first field value, determining the target service data of the user according to the first service data and a first preset value;

[0042] If not, determine the target service data of the user according to the first service data.

[0043] In a second aspect, an embodiment of the present application provides a business data processing device, which is applied to a main server, and the device includes:

[0044] A receiving module, used to receive service request information sent by a terminal device, wherein the service request information includes a user identifier;

[0045] A first determining module, configured to determine service data according to the user identifier, wherein the service data includes first service data and second service data;

[0046] A second determination module, used to determine the user type of the user according to the user identifier;

[0047] A first prediction module, configured to determine first prediction data according to the user type and the first service data;

[0048] A second prediction module, used to determine second prediction data according to the user identifier and the second service data;

[0049] A processing module is used to determine the target service data of the user according to the first prediction data and the second prediction data, and send the target service data to the terminal device.

[0050] In a possible implementation, the first prediction module is specifically used to:

[0051] Determining whether the user type is the first type;

[0052] If yes, determining first feature data and second feature data according to the first business data, and determining the sum of the first feature data and the second feature data as first prediction data;

[0053] If not, first characteristic data is determined according to the first business data, and the first characteristic data is determined as the first prediction data.

[0054] In a possible implementation manner, the first business data includes income data, income-related data, company data, and company-related data, and the first prediction module is specifically used to:

[0055] Inputting the income-related data into a data processing model to obtain processed data corresponding to the data processing model;

[0056] When the difference between the processed data and the income data is within a preset range, determining the income data as the first feature data;

[0057] Determine the target proportion of users according to the company data;

[0058] The second characteristic data is determined according to the company-related data and the target proportion.

[0059] In a possible implementation manner, the second business data includes non-revolving loan data, and the second prediction module is specifically configured to:

[0060] Generate a data query request according to the user identifier, and send the data query request to the first server;

[0061] receiving a data query response sent by the first server, wherein the data query response includes query data;

[0062] According to the query data, the non-revolving loan data is verified to obtain a verification result, wherein the verification result includes a verification success and a verification failure;

[0063] When the verification result is successful, the non-revolving loan data is input into a data prediction model to obtain a predicted value of the data prediction model, and the predicted value is determined as the second predicted data.

[0064] In a possible implementation manner, the processing module is specifically used for:

[0065] Determine the maximum value between the first prediction data and the second prediction data as target prediction data;

[0066] determining a target value according to the second business data;

[0067] Acquire at least one data field corresponding to the first business data;

[0068] The target business data of the user is determined according to the target prediction data, the target value and the at least one data field.

[0069] In a possible implementation manner, the second business data includes non-revolving loan data and revolving loan data, and the processing module is specifically used to:

[0070] Determining a first average value corresponding to a first period based on the non-revolving loan data;

[0071] Determining a second average value corresponding to the first period based on the revolving loan data;

[0072] Determine the product of the second average value and the first preset value as a first value;

[0073] Determine a second value according to the revolving loan data and a second preset value;

[0074] A sum of the first average value, the first value, and the second value is determined as a target value.

[0075] In a possible implementation manner, the processing module is specifically used for:

[0076] Determining first business data according to the target prediction data and the target value;

[0077] Performing identification processing on the at least one data field to determine whether a target field exists;

[0078] If yes, when the field value corresponding to the target field is the first field value, determining the target service data of the user according to the first service data and a first preset value;

[0079] If not, determine the target service data of the user according to the first service data.

[0080] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor, and a transceiver;

[0081] The memory stores computer-executable instructions;

[0082] The processor executes the computer-executable instructions stored in the memory to implement the business data processing method as described in any one of the first aspects.

[0083] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the business data processing method described in any one of the first aspects.

[0084] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the business data processing method described in any one of the first aspects.

[0085] The embodiment of the present application provides a business data processing method, device, equipment and storage medium, in which the method receives business request information sent by a terminal device, the business request information includes a user identifier, determines business data according to the user identifier, the business data includes first business data and second business data, determines the user type of the user according to the user identifier, determines first prediction data according to the user type and the first business data, determines second prediction data according to the user identifier and the second business data, determines the target business data of the user according to the first prediction data and the second prediction data, and sends the target business data to the terminal device. In this way, the user can quickly obtain the target business data, greatly reducing the occupation of human resources, improving user experience, and improving business processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] 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.

[0087] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0088] Figure 2 A flowchart of a method for processing business data provided by an embodiment of the present application;

[0089] Figure 3 A flowchart of another method for processing business data provided by an embodiment of the present application;

[0090] Figure 4 A flowchart of another method for processing business data provided in an embodiment of the present application;

[0091] Figure 5 A schematic diagram of the structure of a business data processing device provided in an embodiment of the present application;

[0092] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0093] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0095] It should be noted that, although the terms "first", "second", etc. are used to describe various information in the embodiments of the present application, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. Optionally, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information.

[0096] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and the user's business data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0097] It should be understood that the terms "comprises" and "includes" indicate the presence of the previously mentioned features, steps, operations, but do not exclude the presence, occurrence or addition of one or at least one other feature, step, operation. The terms "and / or" and the like used in this application may be interpreted as inclusive, or mean any one or any combination. Optionally, "A and / or B" means "any of the following: A; B; A and B". In addition, the character " / " in this article generally indicates that the objects associated before and after are in an "or" relationship.

[0098] When users apply for target business, they need to provide relevant data for proof. For example, when users apply for loan amount, they need to provide relevant financial proof so that the review center can determine the specific loan amount.

[0099] In the related art, the process of a user handling a target business is as follows: the user provides the corresponding data according to the system prompts, waits for manual review, and obtains the target business data after manual review. If the user believes that the target business data does not meet the requirements, additional data needs to be supplemented and then waits for manual review.

[0100] In the above situation, the processing procedure of the target business is relatively long, resulting in low business processing efficiency.

[0101] In order to solve the above technical problems, the embodiment of the present application provides a business data processing method, which receives a business request sent by a terminal device, determines business data according to a user identifier, determines first prediction data and second prediction data according to the business data, determines the user's target business data according to the first prediction data and the second prediction data, and sends the target business data to the terminal device. In this way, the user can quickly obtain the target business data, which greatly reduces the occupation of human resources, improves user experience, and improves business handling efficiency.

[0102] Next, combine Figure 1 , and give examples to illustrate the application scenarios of business data processing.

[0103] Figure 1 This is a schematic diagram of an application scenario provided by the embodiment of this application. Figure 1 , Figure 1 The service processing system 100 may include a terminal device 101 and a main server 102 .

[0104] The terminal device 101 may be any form of terminal, for example, a mobile phone, a computer, a tablet, a wearable device, etc., and the present application embodiment does not specifically limit the electronic device. The terminal device 101 may be equipped with a business system, so that the user can obtain target business data through the terminal device 101. The business system may be written in languages ​​such as C / C++, Java, Shell, or Python, and is not specifically limited here.

[0105] The main server 102 may provide a database, which may store user information, multiple preset models, multiple preset algorithms, etc. The main server 102 may be a cloud server, a server of a distributed system, or a server combined with a blockchain, which is not specifically limited here.

[0106] A communication connection is established between the terminal device 101 and the main server 102. For example, the terminal device 101 can establish a communication connection with the main server 102 via a protocol such as Hypertext Transfer Protocol (HTTP) or Hypertext Transfer Protocol over Secure Socket Layer (HTTPS), which is not specifically limited here.

[0107] The technical solution shown in the present application is described in detail below through specific embodiments. It should be noted that the following embodiments can exist independently or in combination with each other, and the same or similar contents will not be described repeatedly in different embodiments.

[0108] Figure 2 This is a flow chart of a business data processing method provided by an embodiment of the present application. The execution subject of the embodiment of the present application can be a main server or a processor in the main server. Figure 2 , the method comprising:

[0109] S201. Receive service request information sent by a terminal device.

[0110] The service request information includes a user identifier.

[0111] The user ID can be used to identify the user.

[0112] The terminal device can be used to obtain the user's interactive information, and the interactive information may include service requests, user information, etc., which are not limited here.

[0113] The service request packet sent by the terminal device can be received, the service request packet is parsed and processed to obtain the service request information.

[0114] S202: Determine business data according to the user identifier.

[0115] The business data includes first business data and second business data.

[0116] The first business data may be used to represent income-related data of the user.

[0117] The second business data may be used to represent the user's debt-related data.

[0118] The user data corresponding to the user identifier may be searched in the storage space, and the service data corresponding to the service request information may be determined in the user data.

[0119] S203: Determine the user type of the user according to the user identifier.

[0120] The user type may include a first type and a second type.

[0121] The first type can be used to represent employer and employee types.

[0122] The second type may be used to represent the employee type.

[0123] The user data corresponding to the user identification may be queried in the storage space, and the user type of the user may be determined in the user data.

[0124] S204: Determine first prediction data according to the user type and the first service data.

[0125] The first prediction data may be used to represent revenue data predicted based on the business data.

[0126] It can be determined whether the user type is the first type, and a determination result can be obtained. The first prediction data can be determined based on the determination result and the first business data.

[0127] Optionally, the first prediction data can be determined according to the user type and the first business data in the following manner: determine whether the user type is the first type; if so, determine the first characteristic data and the second characteristic data according to the first business data, and determine the sum of the first characteristic data and the second characteristic data as the first prediction data; if not, determine the first characteristic data according to the first business data, and determine the first characteristic data as the first prediction data.

[0128] Optionally, the first service data may include data corresponding to multiple types. If the user does not have data of a certain type, it can be determined that the data of this type is a preset value.

[0129] S205: Determine second prediction data according to the user identifier and the second service data.

[0130] The second prediction data may be used to represent revenue data predicted based on the business data.

[0131] For example, the second prediction data may be information on the loan amount predicted based on the user's debt situation.

[0132] The second business data may be verified according to the user identifier, and after the verification is passed, the second prediction data may be determined according to the second business data.

[0133] Optionally, the second business data may be input into a data prediction model to obtain second prediction data.

[0134] Optionally, the second service data may include data corresponding to multiple types. If the user does not have data of a certain type, it can be determined that the data of this type is a preset value.

[0135] Optionally, the second business data includes non-revolving loan data, and the second prediction data can be determined based on the user identifier and the second business data in the following manner: generate a data query request based on the user identifier, and send the data query request to the first server; receive a data query response sent by the first server; verify the non-revolving loan data based on the query data to obtain a verification result; when the verification result is successful, input the non-revolving loan data into a data prediction model to obtain a predicted value of the data prediction model, and determine the predicted value as the second prediction data.

[0136] Among them, non-revolving loan data can be used to represent mortgage, car loan and other loan data.

[0137] The data query request may be used to request relevant data about the user from the first server.

[0138] For example, the first server may be a bank, and the data query request may be used to query the user's loan data in the bank.

[0139] The data query response includes the query data.

[0140] The data query response may be used to indicate the relevant data queried by the first server based on the user identification, and response information generated based on the relevant data.

[0141] The verification results include verification success and verification failure.

[0142] The data prediction model can be a pre-trained model.

[0143] The data prediction model can be used to predict the income data corresponding to the non-revolving loan data.

[0144] S206. Determine the target service data of the user according to the first prediction data and the second prediction data, and send the target service data to the terminal device.

[0145] The target business data can be used to represent the business data that the user wants to obtain.

[0146] For example, the target business data may be used to indicate the maximum loan amount that a user can obtain.

[0147] The maximum value between the first predicted data and the second predicted data can be determined, and the upper limit value can be obtained to determine whether the maximum value is greater than the upper limit value. If so, the upper limit value is determined as the user's target business data. If not, the maximum value is determined as the user's target business data.

[0148] Optionally, the user's target business data can be determined based on the first prediction data and the second prediction data in the following manner: determine the maximum value between the first prediction data and the second prediction data as the target prediction data; determine the target value based on the second business data; obtain at least one data field corresponding to the first business data; determine the user's target business data based on the target prediction data, the target value and at least one data field.

[0149] The service data processing method provided in this embodiment receives service request information sent by a terminal device, the service request information includes a user identifier, determines service data according to the user identifier, the service data includes first service data and second service data, determines the user type of the user according to the user identifier, determines first prediction data according to the user type and the first service data, determines second prediction data according to the user identifier and the second service data, determines target service data of the user according to the first prediction data and the second prediction data, and sends the target service data to the terminal device. In this way, the user can quickly obtain the target service data, which greatly reduces the occupation of human resources, improves user experience, and improves service handling efficiency.

[0150] Next, combine Figure 3 , a process (S204) of determining first prediction data according to the user type and the first business data is explained, wherein the first business data includes income data, income-related data, company data and company-related data.

[0151] Figure 3 A flowchart of another business data processing method provided in an embodiment of the present application. Based on the above embodiment, see Figure 3 , the method comprising:

[0152] S301: Determine whether the user type is the first type.

[0153] If yes, execute S302;

[0154] If not, execute S306.

[0155] The first type can be used to represent employer and employee types.

[0156] It may be determined whether the type identifier corresponding to the user type is the type identifier corresponding to the first type.

[0157] S302: Input the income-related data into the data processing model to obtain the processed data corresponding to the data processing model.

[0158] Income-related data may be used to represent data associated with income.

[0159] For example, income-related data can be provident fund data or tax data.

[0160] The data processing model can predict corresponding income data based on income-related data.

[0161] The processed data may be used to represent the revenue data predicted by the data processing model.

[0162] The income-related data may be pre-processed to obtain input data, and the input data may be input into a data processing model to obtain processed data corresponding to the data processing model.

[0163] S303: When the difference between the processed data and the income data is within a preset range, the income data is determined as the first feature data.

[0164] The preset range may be a pre-set range.

[0165] The difference between the processed data and the income data can be determined to determine whether the difference is less than or equal to a preset range. If so, it is determined that the difference between the processed data and the income data is within the preset range, and the income data is determined as the first feature data.

[0166] S304. Determine the target proportion corresponding to the user according to the company data.

[0167] The company data may be used to represent data of the company corresponding to the user.

[0168] The target percentage can be used to indicate the proportion of shares held by the user in the company.

[0169] The company data can be analyzed and processed to determine the target proportion corresponding to the user.

[0170] S305. Determine the second characteristic data according to the company-related data and the target proportion.

[0171] Company-related data can be used to represent data such as the company's operating profit.

[0172] The second characteristic data can be used to represent the operating profit data corresponding to the user.

[0173] The company-related data can be multiplied by the target proportion to determine the second characteristic data.

[0174] S306: Determine first characteristic data according to the first business data, and determine the first characteristic data as first prediction data.

[0175] The first business data includes income data and income-related data.

[0176] The income-related data may be input into the data processing model to obtain processed data corresponding to the data processing model. When the difference between the processed data and the income data is within a preset range, the income data is determined as the first characteristic data.

[0177] The implementation content of each step in the embodiment of the present application can refer to the description of the corresponding steps or operations in the above method embodiment, and repeated content will not be repeated.

[0178] The business data processing method provided in this embodiment determines whether the user type is the first type; if so, inputs the income-related data into the data processing model to obtain the processing data corresponding to the data processing model; when the difference between the processing data and the income data is within the preset range, the income data is determined as the first characteristic data; according to the company data, the target proportion corresponding to the user is determined; according to the company-related data and the target proportion, the second characteristic data is determined, and the sum of the first characteristic data and the second characteristic data is determined as the first prediction data; if not, the first characteristic data is determined according to the first business data, and the first characteristic data is determined as the first prediction data. In this way, users can quickly obtain target business data, which greatly reduces the occupation of human resources, improves user experience, and improves business processing efficiency.

[0179] Next, combine Figure 4 , the process (S206) of determining the target business data of the user according to the first prediction data and the second prediction data is explained.

[0180] Figure 4 A flowchart of another method for processing business data provided by an embodiment of the present application. Figure 4 , the method comprising:

[0181] S401. Determine the maximum value between the first prediction data and the second prediction data as the target prediction data.

[0182] Determine whether the first prediction data is greater than or equal to the second prediction data. If so, determine the first prediction data as the target prediction data. If not, determine the second prediction data as the target prediction data.

[0183] S402: Determine a target value according to the second business data.

[0184] The target value can be used to represent the user's debt value.

[0185] The target value can be determined based on the user's loan data.

[0186] A first preset algorithm may be obtained, and a target value may be determined according to the first preset algorithm and the second business data.

[0187] Optionally, the second business data includes non-revolving loan data and revolving loan data, and the target value is determined based on the second business data: based on the non-revolving loan data, a first average value corresponding to the first period is determined; based on the revolving loan data, a second average value corresponding to the first period is determined; the product of the second average value and the first preset value is determined as the first value; the second value is determined based on the revolving loan data and the second preset value; and the sum of the first average value, the first value, and the second value is determined as the target value.

[0188] Among them, revolving loan data can be used to represent loan data about credit cards.

[0189] Non-revolving loan data can be used to represent mortgage, car loan and other loan data.

[0190] The first period may be a preset duration.

[0191] The first average value may be used to represent the average value of the non-revolving loan data corresponding to the first period.

[0192] For example, the first cycle is one month, and the first average value is the corresponding non-revolving loan data for each month.

[0193] The second average value may be used to represent the average of the revolving loan data to a corresponding value within the first period.

[0194] The first preset value and the second preset value may be preset.

[0195] S403: Obtain at least one data field corresponding to the first business data.

[0196] A data field may represent the definition and format of data.

[0197] Field parsing may be performed on the first business data to determine at least one data field corresponding to the first business data.

[0198] S404: Determine first business data according to the target prediction data and the target value.

[0199] A second preset algorithm may be obtained, and the first business data may be determined according to the second preset algorithm, the target prediction data, and the target value.

[0200] Optionally, the first service data may be determined by the following formula:

[0201]

[0202] Wherein, A may represent first business data, X may represent target prediction data, Y may represent a target value, and r may represent a preset interest rate.

[0203] S405: Perform identification processing on at least one data field to determine whether a target field exists.

[0204] If yes, execute S406;

[0205] If not, execute S407.

[0206] At least one data field may be subjected to recognition processing to obtain a recognition result, and it may be determined whether a target field exists in the recognition result.

[0207] S406: When the field value corresponding to the target field is the first field value, determine the target business data of the user according to the first business data and the first preset value.

[0208] The first field value may be a preset value.

[0209] For example, the first field value may be used to indicate that there is no asset pledge.

[0210] The field value corresponding to the target field may be determined to determine whether the field value is the first field value, and if so, the target business data of the user may be determined based on the first business data and the first preset value.

[0211] Optionally, the user's target business data can be determined based on the first business data and the first preset value in the following manner: add the value corresponding to the first business data to the first preset value to obtain the target value, and determine whether the target value is greater than the second preset value; if so, determine the second preset value as the user's target business data; if not, determine the target value as the user's target business data.

[0212] S407: Determine target service data of the user according to the first service data.

[0213] It can be determined whether the value corresponding to the first business data is greater than the second preset value. If so, the second preset value is determined as the user's target business data; if not, the first business data is determined as the user's target business data.

[0214] The implementation content of each step in the embodiment of the present application can refer to the description of the corresponding steps or operations in the above method embodiment, and repeated content will not be repeated.

[0215] The business data processing method provided in this embodiment determines the maximum value between the first prediction data and the second prediction data as the target prediction data, determines the target value according to the second business data, obtains at least one data field corresponding to the first business data, determines the user's target business data according to the target prediction data, the target value and at least one data field, determines the first business data according to the target prediction data and the target value, performs identification processing on at least one data field, determines whether there is a target field, and if so, determines the user's target business data according to the first business data and the first preset value when the field value corresponding to the target field is the first field value, and if not, determines the user's target business data according to the first business data. In this way, the user can quickly obtain the target business data, greatly reducing the occupation of human resources, improving user experience, and improving business processing efficiency.

[0216] Figure 5 This is a schematic diagram of the structure of a business data processing device provided in an embodiment of the present application. Figure 5 The service data processing device 500 includes a receiving module 501, a first determining module 502, a second determining module 503, a first prediction module 504, a second prediction module 505 and a processing module 506, wherein:

[0217] A receiving module 501 is used to receive service request information sent by a terminal device, wherein the service request information includes a user identifier;

[0218] A first determining module 502, configured to determine service data according to the user identifier, the service data including first service data and second service data;

[0219] A second determination module 503, configured to determine a user type of the user according to the user identifier;

[0220] A first prediction module 504, configured to determine first prediction data according to the user type and the first service data;

[0221] A second prediction module 505, configured to determine second prediction data according to the user identifier and the second service data;

[0222] The processing module 506 is used to determine the target service data of the user according to the first prediction data and the second prediction data, and send the target service data to the terminal device.

[0223] In a possible implementation, the first prediction module 504 is specifically configured to:

[0224] Determining whether the user type is the first type;

[0225] If yes, determining first feature data and second feature data according to the first business data, and determining the sum of the first feature data and the second feature data as first prediction data;

[0226] If not, first characteristic data is determined according to the first business data, and the first characteristic data is determined as the first prediction data.

[0227] In a possible implementation manner, the first business data includes income data, income-related data, company data, and company-related data, and the first prediction module 504 is specifically used to:

[0228] Inputting the income-related data into a data processing model to obtain processed data corresponding to the data processing model;

[0229] When the difference between the processed data and the income data is within a preset range, determining the income data as the first feature data;

[0230] Determine the target proportion of users according to the company data;

[0231] The second characteristic data is determined according to the company-related data and the target proportion.

[0232] In a possible implementation manner, the second business data includes non-revolving loan data, and the second prediction module 505 is specifically configured to:

[0233] Generate a data query request according to the user identifier, and send the data query request to the first server;

[0234] receiving a data query response sent by the first server, wherein the data query response includes query data;

[0235] According to the query data, the non-revolving loan data is verified to obtain a verification result, wherein the verification result includes a verification success and a verification failure;

[0236] When the verification result is successful, the non-revolving loan data is input into a data prediction model to obtain a predicted value of the data prediction model, and the predicted value is determined as the second predicted data.

[0237] In a possible implementation, the processing module 506 is specifically configured to:

[0238] Determine the maximum value between the first prediction data and the second prediction data as target prediction data;

[0239] determining a target value according to the second business data;

[0240] Acquire at least one data field corresponding to the first business data;

[0241] The target business data of the user is determined according to the target prediction data, the target value and the at least one data field.

[0242] In a possible implementation manner, the second business data includes non-revolving loan data and revolving loan data, and the processing module 506 is specifically used to:

[0243] Determining a first average value corresponding to a first period based on the non-revolving loan data;

[0244] Determining a second average value corresponding to the first period based on the revolving loan data;

[0245] Determine the product of the second average value and the first preset value as a first value;

[0246] Determine a second value according to the revolving loan data and a second preset value;

[0247] A sum of the first average value, the first value, and the second value is determined as a target value.

[0248] In a possible implementation, the processing module 506 is specifically configured to:

[0249] Determining first business data according to the target prediction data and the target value;

[0250] Performing identification processing on the at least one data field to determine whether a target field exists;

[0251] If yes, when the field value corresponding to the target field is the first field value, determining the target service data of the user according to the first service data and a first preset value;

[0252] If not, determine the target service data of the user according to the first service data.

[0253] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 , the electronic device 600 may include: a memory 601 , a processor 602 , and a transceiver 603 .

[0254] The memory 601 is used to store program instructions;

[0255] The processor 602 is used to execute the program instructions stored in the memory, so as to enable the electronic device 600 to execute any of the business data processing methods shown above.

[0256] The transceiver 603 may include: a transmitter and / or a receiver. The transmitter may also be referred to as a transmitter, a transmitter, a transmission port, a transmission interface, or the like, and the receiver may also be referred to as a receiver, a reception port, a reception interface, or the like. Exemplarily, the memory 601, the processor 602, and the transceiver 603 are interconnected via a bus 604.

[0257] The embodiment of the present application also provides a computer program product, which can be executed by a processor. When the computer program product is executed, the above-mentioned business data processing method can be implemented.

[0258] The business data processing device, electronic device, computer-readable storage medium and computer program product of the embodiments of the present application can execute the technical solutions shown in the above-mentioned business data processing method embodiments. Their implementation principles and beneficial effects are similar and will not be repeated here.

[0259] All or part of the steps of the above-mentioned method embodiments can be completed by hardware related to program instructions. The above-mentioned program can be stored in a readable memory. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the above-mentioned memory (storage medium) includes: read-only memory (ROM), random access memory (RAM), flash memory, hard disk, solid state drive, magnetic tape, floppy disk, optical disc and any combination thereof.

[0260] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processing unit of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0261] These computer executable instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0262] These computer executable instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0263] Obviously, those skilled in the art can make various changes and modifications to the embodiments of the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the embodiments of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A business data processing method, characterized in that: Applied to the main server, the method comprises: Receiving service request information sent by a terminal device, wherein the service request information includes a user identifier; Determine service data according to the user identifier, the service data including first service data and second service data; Determining the user type of the user according to the user identifier; Determining first prediction data according to the user type and the first service data; Determining second prediction data according to the user identifier and the second service data; The target service data of the user is determined according to the first prediction data and the second prediction data, and the target service data is sent to the terminal device.

2. The method according to claim 1, characterized in that Determining first prediction data according to the user type and the first service data includes: Determining whether the user type is the first type; If yes, determining first feature data and second feature data according to the first business data, and determining the sum of the first feature data and the second feature data as first prediction data; If not, first characteristic data is determined according to the first business data, and the first characteristic data is determined as the first prediction data.

3. The method according to claim 2, characterized in that The first business data includes income data, income-related data, company data and company-related data. According to the first business data, first feature data and second feature data are determined, including: Inputting the income-related data into a data processing model to obtain processed data corresponding to the data processing model; When the difference between the processed data and the income data is within a preset range, determining the income data as the first feature data; Determine the target proportion of users according to the company data; The second characteristic data is determined according to the company-related data and the target proportion.

4. The method according to claim 2 or 3, characterized in that: The second business data includes non-revolving loan data, and determining second prediction data according to the user identifier and the second business data includes: Generate a data query request according to the user identifier, and send the data query request to the first server; receiving a data query response sent by the first server, wherein the data query response includes query data; According to the query data, the non-revolving loan data is verified to obtain a verification result, wherein the verification result includes a verification success and a verification failure; When the verification result is successful, the non-revolving loan data is input into a data prediction model to obtain a predicted value of the data prediction model, and the predicted value is determined as the second predicted data.

5. The method according to any one of claims 1 to 4, characterized in that: Determining target service data of the user according to the first prediction data and the second prediction data includes: Determine the maximum value between the first prediction data and the second prediction data as target prediction data; determining a target value according to the second business data; Acquire at least one data field corresponding to the first business data; The target business data of the user is determined according to the target prediction data, the target value and the at least one data field.

6. The method according to claim 5, characterized in that The second business data includes non-revolving loan data and revolving loan data. Determining a target value according to the second business data includes: Determining a first average value corresponding to a first period based on the non-revolving loan data; Determining a second average value corresponding to the first period based on the revolving loan data; Determine the product of the second average value and the first preset value as a first value; Determine a second value according to the revolving loan data and a second preset value; A sum of the first average value, the first value, and the second value is determined as a target value.

7. The method according to claim 5 or 6, characterized in that: Determining target service data of the user according to the target prediction data, the target value and the at least one data field includes: Determining first business data according to the target prediction data and the target value; Performing identification processing on the at least one data field to determine whether a target field exists; If yes, when the field value corresponding to the target field is the first field value, determining the target service data of the user according to the first service data and a first preset value; If not, determine the target service data of the user according to the first service data.

8. A business data processing device, characterized in that: Applied to a main server, the device comprises: A receiving module, used to receive service request information sent by a terminal device, wherein the service request information includes a user identifier; A first determining module, configured to determine service data according to the user identifier, wherein the service data includes first service data and second service data; A second determination module, used to determine the user type of the user according to the user identifier; A first prediction module, configured to determine first prediction data according to the user type and the first service data; A second prediction module, used to determine second prediction data according to the user identifier and the second service data; A processing module is used to determine the target service data of the user according to the first prediction data and the second prediction data, and send the target service data to the terminal device.

9. An electronic device, characterized in that: include: memory, processors, and transceivers; Wherein, the memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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