Business processing method and device, equipment, medium and program product

Abstract

The invention provides a business processing method which can be applied to the Internet field, the risk assessment field and the financial field. The business processing method comprises the following steps: obtaining object information of a target object from a plurality of different channels to obtain a plurality of object information sets; determining an evaluation parameter value of an evaluation parameter matched with the object information set; the information credibility of the object information set is determined through a credibility evaluation formula and the evaluation parameter values, multiple formula information credibility is obtained, and the credibility evaluation formula comprises evaluation parameters and weights matched with the evaluation parameters; determining a target object information set from the plurality of object information sets based on the credibility of the plurality of formula information; and performing service processing on the to-be-processed service of the target object based on the target object information set. The invention further provides a business processing device, equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the fields of Internet, risk assessment and finance, and in particular to a business processing method, device, equipment, medium and program product. Background Art

[0002] In recent years, with the development of big data technology, a large amount of customer information has poured in. The customer information stored in the database has at least one of the following characteristics: the source is complex and diverse, some information is incomplete, and customer information such as telephone number and work location changes dynamically. As a result, the credibility of customer information is difficult to guarantee.

[0003] In the process of realizing the inventive concept of the present disclosure, the inventors found that the application of related technologies has at least the following problems: using customer information whose credibility is difficult to guarantee to process business, the effectiveness and security of business processing cannot be guaranteed. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a business processing method, apparatus, device, storage medium and program product.

[0005] According to a first aspect of the present disclosure, a service processing method is provided, including:

[0006] Obtain object information of a target object from multiple different channels to obtain multiple object information sets; determine evaluation parameter values ​​of evaluation parameters that match the object information set; determine information credibility of the object information set using a credibility evaluation formula and the evaluation parameter values ​​to obtain multiple formula information credibility, wherein the credibility evaluation formula includes evaluation parameters and weights that match the evaluation parameters; determine a target object information set from multiple object information sets based on the multiple formula information credibility; and perform business processing for pending business of the target object based on the target object information set.

[0007] According to an embodiment of the present disclosure, determining an evaluation parameter value of an evaluation parameter that matches an object information set includes: determining business identification information for characterizing a business type from the object information set when it is determined that the object information set includes information for processing a business; determining business type information of the business to be processed; and determining an evaluation parameter value of a business background parameter based on the business identification information and the business type information.

[0008] According to an embodiment of the present disclosure, determining an evaluation parameter value of an evaluation parameter that matches an object information set may also include: determining information generation identification information and information acquisition identification information from the object information set, wherein the information generation identification information represents a generation channel of the object information set, and the information acquisition identification information represents an acquisition channel of the object information set; determining an evaluation parameter value of a channel parameter based on an evaluation parameter value of a first channel parameter and an evaluation parameter value of a second channel parameter, wherein the evaluation parameter value of the first channel parameter is determined based on the information generation identification information and predetermined generation standard information, and the evaluation parameter value of the second channel parameter is determined based on the information acquisition identification information and predetermined acquisition standard information.

[0009] According to an embodiment of the present disclosure, determining an evaluation parameter value of an evaluation parameter that matches an object information set may also include: determining an information generation time and a validity period of the object information set; determining an information decay rate based on the information generation time and the current time; determining a validity parameter value based on the validity period and the information generation time; and obtaining an evaluation parameter value of a timeliness parameter based on the information decay rate and the validity parameter value.

[0010] According to an embodiment of the present disclosure, a credibility evaluation formula is obtained in the following manner: object information of sample objects is obtained from multiple different channels to obtain multiple sample object information sets; based on each sample object information set, sample evaluation parameter values ​​and sample labels of sample evaluation parameters that match the sample object information set are determined, wherein the sample labels characterize the information credibility of the sample object information set; multiple sample evaluation parameter values, multiple sample labels and prompt information are input into a large language model to obtain a credibility evaluation formula, wherein the prompt information is used to instruct the large language model to perform a formula generation task and output expected results.

[0011] According to an embodiment of the present disclosure, the credibility evaluation formula can also be obtained in the following manner: obtaining object information of sample objects from multiple different channels to obtain multiple sample object information sets; based on each sample object information set, determining sample evaluation parameter values ​​and sample labels of sample evaluation parameters that match the sample object information set, wherein the sample labels characterize the information credibility of the sample object information set; and performing function fitting on multiple sample evaluation parameter values ​​and multiple sample labels to obtain the credibility evaluation formula.

[0012] According to an embodiment of the present disclosure, the credibility of the formula information of each object information set is determined using a credibility evaluation formula and an evaluation parameter value, including: based on the evaluation parameter, substituting the evaluation parameter value into the credibility evaluation formula to obtain the credibility of the formula information of each object information set.

[0013] The second aspect of the present disclosure provides a business processing device, including an object information set generation module, an evaluation parameter value determination module, an information credibility determination module, a target object information set determination module, and a business processing module. The object information set generation module is used to obtain object information of a target object from multiple different channels, thereby obtaining multiple object information sets, the evaluation parameter value determination module is used to determine the evaluation parameter value of the evaluation parameter matching the object information set, the information credibility determination module is used to determine the information credibility of the object information set using the credibility evaluation formula and the evaluation parameter value, thereby obtaining multiple formula information credibility, the target object information set determination module is used to determine the target object information set from multiple object information sets based on multiple formula information credibility, and the business processing module is used to perform business processing for the target object's pending business based on the target object information set.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0016] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor.

[0017] According to an embodiment of the present disclosure, by utilizing object information of a target object obtained from multiple different channels, multiple object information sets are obtained; evaluation parameter values ​​of evaluation parameters matching the object information sets are determined; information credibility of the object information set is determined using a credibility evaluation formula and evaluation parameter values, and multiple formula information credibility is obtained, wherein the credibility evaluation formula includes evaluation parameters and weights matching the evaluation parameters; based on multiple formula information credibility, a target object information set is determined from multiple object information sets; a technical means for performing business processing for a pending business of the target object based on the target object information set can determine the formula information credibility using the credibility evaluation formula and evaluation parameter values, so that the quantitative standard of credibility is unified, and in addition, based on multiple formula information credibility, a target object information set is determined from multiple object information sets, and multiple formula information credibility can be used as screening labels to screen the object information set, thereby ensuring that the information of the screened target object information set is accurate and effective, thereby ensuring the security and effectiveness of business processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above contents 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:

[0019] Figure 1 A diagram schematically shows an application scenario of a service processing method and device according to an embodiment of the present disclosure;

[0020] Figure 2 A flowchart of a business processing method according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 3 A flowchart for determining evaluation parameter values ​​of business context parameters according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 4 A schematic diagram schematically shows an object information set according to an embodiment of the present disclosure;

[0023] Figure 5 A structural block diagram schematically shows a service processing method and device according to an embodiment of the present disclosure; and

[0024] Figure 6 A block diagram of an electronic device suitable for implementing a service processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0025] 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.

[0026] 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 features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0027] 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.

[0028] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly 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.).

[0029] In the technical solution disclosed in the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0030] In recent years, with the development of big data technology, banks have begun to use big data analysis methods to integrate customers' credit records, transaction records, social relationships and other multi-dimensional data, so that banks can have a more comprehensive understanding of customers' credit status and behavioral habits.

[0031] In the process of implementing the invention concept disclosed in this disclosure, the inventors found that the application of related technologies has at least the following problems: The credibility assessment of customer information is not comprehensive, accurate, and efficient, which further affects the security and effectiveness of business processing.

[0032] In view of this, the present disclosure addresses the above technical problems by integrating object information sets of target objects obtained from multiple different channels, determining evaluation parameters and evaluation parameter values ​​that match the object information sets, and utilizing a credibility evaluation formula to process the evaluation parameter values ​​to determine the information credibility of each object information set. This improves the evaluation accuracy while also improving processing efficiency, thereby ensuring the security and effectiveness of business processing.

[0033] Specifically, an embodiment of the present disclosure provides a business processing method and device, including: acquiring object information of a target object from multiple different channels to obtain multiple object information sets; determining evaluation parameter values ​​of evaluation parameters that match the object information set; determining the information credibility of the object information set using a credibility evaluation formula and the evaluation parameter values ​​to obtain multiple formula information credibility, wherein the credibility evaluation formula includes evaluation parameters and weights that match the evaluation parameters; determining a target object information set from multiple object information sets based on multiple formula information credibility; and performing business processing for the target object's pending business based on the target object information set.

[0034] In this way, the problems of low comprehensiveness, low accuracy and low efficiency in credibility assessment of customer information in situations such as diverse information update sources, business processing interruptions, and difficulty in tracing the background of information collection can be solved, thereby ensuring the security and effectiveness of business processing.

[0035] It should be noted that the business processing method and device provided in the embodiments of the present disclosure can be used in the Internet field and the financial field. The request processing method and device provided in the embodiments of the present disclosure can also be used in any field other than the Internet field and the financial field. The application field of the request processing method and device provided in the embodiments of the present disclosure is not limited.

[0036] Figure 1 The application scenario diagram of the business processing method and device according to the embodiments of the present disclosure is schematically shown.

[0037] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a terminal device 101, a terminal device 102, a terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0038] 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, social platform software, etc. (only examples).

[0039] 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.

[0040] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed by users using the 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.

[0041] It should be noted that the service processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the service processing device provided in the embodiment of the present disclosure can generally be set in the server 105. The service processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the service processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.

[0042] 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.

[0043] The following will be based on Figure 1 The scene described by Figure 2 to Figure 6 The service processing device and method of the disclosed embodiment are described in detail.

[0044] Figure 2 The flowchart of the service processing method according to the embodiment of the present disclosure is schematically shown.

[0045] like Figure 2 As shown, the business processing method includes operations S210 to S250.

[0046] In operation S210, object information of a target object is acquired from a plurality of different channels to obtain a plurality of object information sets.

[0047] In an embodiment of the present disclosure, before obtaining the user's information, the user's consent or authorization may be obtained. For example, before operation S210, a request to obtain the user's information may be issued to the user. When the user agrees or authorizes that the user's information may be obtained, operation S210 is performed.

[0048] According to an embodiment of the present disclosure, a channel may be a channel for obtaining customer information, and may include internal offline channels, internal online channels, external offline channels, and external online channels. Internal offline channels may include teller services at business outlets and self-service equipment, internal online channels may include mobile banking, external offline channels may include offline outlets of cooperative institutions, and external online channels may include information flow advertising, operator interfaces, and cooperative institution interfaces.

[0049] According to an embodiment of the present disclosure, the target object may be a customer, which may include an individual customer and a non-individual customer. The non-individual customer may include a government agency customer, an institution customer, a social group customer, and an enterprise customer.

[0050] According to an embodiment of the present disclosure, the object information of the target object may include basic object information, service identification information, service type information, information generation identification information, information acquisition identification information, information generation time, validity period, etc. The above basic object information may include name, contact number, address, etc.

[0051] According to an embodiment of the present disclosure, the object information set may be information associated with the same target object stored in a set form, and may include one or more object information of the target object.

[0052] According to an embodiment of the present disclosure, after acquiring object information of a target object from multiple different channels, multiple object information sets are obtained.

[0053] According to an embodiment of the present disclosure, in response to receiving a request from a target object for handling a business, object information of the target object can be obtained from multiple different channels to obtain multiple object information sets. The business risk of handling the business can be determined using the object information of the target object.

[0054] Generally speaking, the credibility of object information sets from different channels is different. The object information set with the highest credibility or the credibility that meets the standard can be determined from multiple object information sets and used as reference information to handle business for the target object.

[0055] In operation S220, an evaluation parameter value of the evaluation parameter matched with the object information set is determined.

[0056] According to an embodiment of the present disclosure, the evaluation parameter value of the evaluation parameter that is determined to match the object information set may be an evaluation parameter value obtained based on the selected evaluation parameter.

[0057] In operation S230, the information credibility of the object information set is determined using a credibility evaluation formula and evaluation parameter values ​​to obtain a plurality of formula information credibility, wherein the credibility evaluation formula includes evaluation parameters and weights matching the evaluation parameters.

[0058] According to an embodiment of the present disclosure, the formula information credibility may include one or more of a score, a rating, and a verification result of the credibility of the object information set.

[0059] According to an embodiment of the present disclosure, the evaluation parameter may be an indicator parameter for evaluating the credibility of the object information set or may be understood as an evaluation factor. The evaluation parameter may be proposed for one object information in the object information set, but is not limited thereto, and may also be proposed for multiple object information in the object information set.

[0060] For example, the evaluation parameters may include a channel parameter for indicating the source channel of information in the object information set, but are not limited thereto, and may also include a business context parameter for indicating whether the business types of the business indicated by the object information set and the pending business are consistent.

[0061] The evaluation parameters can be extracted from the object information set, and the corresponding evaluation parameter values ​​can be obtained according to the evaluation rules. The evaluation rules are not limited. For example, the evaluation rules may include: if the business types of the business indicated in the object information set and the pending business are consistent, the evaluation parameter value is 1; if the business types of the business indicated in the object information set and the pending business are inconsistent, the evaluation parameter value is 0.

[0062] The weights that match the evaluation parameters are used to characterize the importance of the corresponding evaluation parameters in the credibility of the evaluation information.

[0063] The credibility evaluation formula may include a weighted summation formula. For example, it includes evaluation parameters and weights that match the evaluation parameters. However, it is not limited to this. The credibility evaluation formula may also include other types, such as formulas that simultaneously configure power coefficients and weights for evaluation parameters. As long as the credibility evaluation formula can be used to obtain the credibility of the formula information of the object information set, it will be fine.

[0064] In operation S240, a target object information set is determined from among a plurality of object information sets based on a plurality of formula information credibility levels.

[0065] In operation S250, business processing is performed for the to-be-processed business of the target object based on the target object information set.

[0066] According to the embodiments of the present disclosure, the credibility of formula information is determined by using the credibility evaluation formula and the evaluation parameter value, so that the quantification standard of credibility is unified. In addition, the target object information set is determined from multiple object information sets based on multiple formula information credibility, and the object information set can be screened using multiple formula information credibility as screening labels, thereby ensuring that the information of the screened target object information set is accurate and effective, thereby ensuring the security and effectiveness of business processing.

[0067] Figure 3 The flowchart of determining the evaluation parameter value according to another embodiment of the present disclosure is schematically shown.

[0068] like Figure 3 As shown, the business processing method includes operations S310 to S360.

[0069] In operation S310, object information of a target object is acquired from a plurality of different channels to obtain a plurality of object information sets.

[0070] In operation S320, it is determined whether the object information set includes information for processing the service. If yes, operation S330 is performed, and if no, operation S360 is performed.

[0071] In operation S330, service identification information for characterizing a service type is determined from the object information set.

[0072] According to an embodiment of the present disclosure, the service identification information may be information used to characterize a service type. The service type information of the processed service matching the object information set may be determined based on the service identification information.

[0073] In operation S340, business type information of the business to be processed is determined.

[0074] According to an embodiment of the present disclosure, the business type information may be used to characterize the business type of the business to be processed, such as a credit loan application business.

[0075] In operation S350, an evaluation parameter value of a service context parameter is determined based on the service identification information and the service type information.

[0076] In operation S360, in a case where it is determined that the object information set does not include information for processing the service, the evaluation parameter value of the service context parameter is set to a predetermined evaluation parameter value, for example, 0.

[0077] According to an embodiment of the present disclosure, based on the business type information of the processed business represented by the business identification information and the business type information of the business to be processed, it can be determined whether the corresponding object information set is related to the business to be processed, and the evaluation parameter value of the business background parameter is determined based on the correlation.

[0078] The evaluation parameter value of the service background parameter can be set to a value between 0 and 1. For example, if the object information set is related to the service to be processed, the evaluation parameter value is 1. If the object information set is not related to the service to be processed, the evaluation parameter value is 0.

[0079] According to the embodiments of the present disclosure, by taking business background parameters as evaluation parameters, the relevance of the object information set and the business to be processed can be taken as a reference factor, combined with the actual application scenario, to improve the information screening accuracy and simplify the screening operation.

[0080] According to an embodiment of the present disclosure, determining an evaluation parameter value of an evaluation parameter that matches an object information set may include: determining information generation identification information and information acquisition identification information from the object information set. Determining the evaluation parameter value of the channel parameter based on the evaluation parameter value of the first channel parameter and the evaluation parameter value of the second channel parameter

[0081] According to an embodiment of the present disclosure, the information generation identification information represents a generation channel of the object information set. The information acquisition identification information represents an acquisition channel of the object information set.

[0082] The generation channel may refer to the source of the object information set. For example, if the object information set is automatically filled in by the user when the user registers an account, the generation channel is the user input channel.

[0083] The acquisition channel may refer to the storage location of the object information set. For example, the object information set filled in by the user when registering an account is stored in the customer information database. When handling pending business for the user, the object information set is retrieved from the customer information database. The acquisition channel of the object information set includes the customer information database.

[0084] According to an embodiment of the present disclosure, the evaluation parameter value of the first channel parameter is determined based on the information generation identification information and the predetermined generation standard information. The evaluation parameter value of the second channel parameter is determined based on the information acquisition identification information and the predetermined acquisition standard information.

[0085] According to an embodiment of the present disclosure, the predetermined generation standard information and the predetermined acquisition standard information can be used as reference information, for example, a reliable institution, company or website is predetermined, and the relevant information of the institution, company or website is used as the predetermined generation standard information or the predetermined acquisition standard information.

[0086] The information generation identification information is compared with the predetermined generation standard information to determine whether the two are consistent, thereby obtaining the evaluation parameter value of the first channel parameter. Similarly, the information acquisition identification information is compared with the predetermined acquisition standard information to determine whether the two are consistent, thereby obtaining the evaluation parameter value of the second channel parameter.

[0087] The method for determining the numerical value of the evaluation parameter value of the first channel parameter value and the evaluation parameter value of the second channel parameter is similar to the method for determining the numerical value of the evaluation parameter value of the business background parameter, and will not be repeated here.

[0088] According to the embodiments of the present disclosure, the channels are divided into generation channels and source channels, which can refine the analysis granularity and improve the comprehensiveness of the analysis. In addition, the channels are compared with the standard information to obtain the evaluation parameter value, which can simplify the processing operation and improve the processing accuracy.

[0089] According to an embodiment of the present disclosure, the evaluation parameters may further include timeliness parameters.

[0090] According to an embodiment of the present disclosure, determining an evaluation parameter value of an evaluation parameter that matches an object information set may include: determining an information generation time and a validity period of the object information set. Determining an information decay rate based on the information generation time and the current time. Determining a validity parameter value based on the validity period and the information generation time. Obtaining an evaluation parameter value of a timeliness parameter based on the information decay rate and the validity parameter value.

[0091] According to an embodiment of the present disclosure, the object information set of the target object may further include information generation time and validity period.

[0092] According to an embodiment of the present disclosure, the information generation time of the object information set may include a timestamp of the server system when the object information set is generated.

[0093] According to an embodiment of the present disclosure, the validity period of the object information set may include a validity period set based on a previous change period of the relevant customer information, such as a period node of a change period of the relevant customer information.

[0094] According to an embodiment of the present disclosure, determining the information decay rate based on the information generation time and the current time may include obtaining the information decay rate based on the time interval between the information generation time and the current time. For example, if the time interval is longer, the information decay rate is larger and the effectiveness is smaller. Conversely, if the time interval is shorter, the information decay rate is smaller and the effectiveness is larger.

[0095] According to an embodiment of the present disclosure, determining the validity parameter value based on the information generation time and the validity period may include judging whether the time interval between the current time node and the information generation time exceeds the validity period, and outputting the judgment result.

[0096] According to an embodiment of the present disclosure, obtaining an evaluation parameter value of a timeliness parameter based on an information decay rate and a validity parameter value may include weighted summing the information decay rate and the validity parameter value to obtain the evaluation parameter value of the timeliness parameter.

[0097] According to an embodiment of the present disclosure, an evaluation parameter value of a timeliness parameter is obtained based on the information decay rate and the validity parameter value, thereby improving the granularity and comprehensiveness of the analysis, thereby improving the accuracy of the credibility evaluation.

[0098] According to an embodiment of the present disclosure, the credibility evaluation formula includes evaluation parameters and weights matching the evaluation parameters.

[0099] According to an embodiment of the present disclosure, determining the information credibility of each object information set using a credibility evaluation formula and an evaluation parameter value may include: based on the evaluation parameter, substituting the evaluation parameter value into the credibility evaluation formula to obtain the information credibility of each object information set.

[0100] The credibility evaluation formula includes parameters with evaluation parameters as variables and weights corresponding to the evaluation parameters as coefficients. The evaluation parameter values ​​of the evaluation parameters of the object information set of the target object can be substituted into the parameters with the corresponding evaluation parameters as variables of the credibility evaluation formula to obtain the formula information credibility.

[0101] According to the embodiments of the present disclosure, the credibility evaluation formula is used to process the evaluation parameter value, thereby improving the accuracy and processing efficiency.

[0102] According to an embodiment of the present disclosure, determining a target object information set from multiple object information sets based on multiple formula information credibility may include: selecting an object information set with the highest formula information credibility as the target object information set.

[0103] Perform business processing for pending business of the target object based on the target object information set.

[0104] According to an embodiment of the present disclosure, a credibility evaluation formula can be obtained in the following manner: obtaining object information of sample objects from multiple different channels to obtain multiple sample object information sets; based on each sample object information set, determining sample evaluation parameter values ​​and sample labels of sample evaluation parameters that match the sample object information set, wherein the sample labels characterize the information credibility of the sample object information set; inputting multiple sample evaluation parameter values, multiple sample labels and prompt information into a large language model to obtain a credibility evaluation formula, wherein the prompt information is used to instruct the large language model to perform the formula generation task and output expected results.

[0105] According to an embodiment of the present disclosure, a sample label can represent the information credibility of a sample object information set, which is a true value.

[0106] According to an embodiment of the present disclosure, a sample object information set, a sample evaluation parameter, a sample evaluation parameter value, and Figure 2 The object information sets, evaluation parameters, and evaluation parameter values ​​shown have the same or similar interpretations and processing methods, and are only used to reflect the differences in the embodiments.

[0107] According to an embodiment of the present disclosure, a large language model (LLM) may include one or more of: GPT (Generative Pre-Trained Transformer), GLM (General Language Model), etc. However, it is not limited thereto. The large model may also include a model constructed by a variety of known networks such as convolutional neural networks, recurrent neural networks, and long short-term memory networks.

[0108] According to an embodiment of the present disclosure, the large language model is a model that has been pre-trained and trained using training samples. A large language model that has the ability to generate a credibility evaluation formula.

[0109] According to an embodiment of the present disclosure, the prompt information is used to instruct the large language model to perform a formula generation task and output an expected result, such as outputting a credibility evaluation formula.

[0110] According to an embodiment of the present disclosure, multiple sample evaluation parameter values, multiple sample labels, and prompt information may be input into a large language model to obtain a credibility evaluation formula. However, this is not limited to this, and multiple sample evaluation parameter values, multiple sample evaluation parameters, multiple sample labels, and prompt information may also be input into a large language model to obtain a credibility evaluation formula.

[0111] For example, the sample evaluation parameter value A of the business background parameters, the sample evaluation parameter value A of the channel parameters, the sample evaluation parameter value A of the timeliness parameters, and the sample label A of the sample object information set A; the sample evaluation parameter value B of the business background parameters, the sample evaluation parameter value B of the channel parameters, the sample evaluation parameter value B of the timeliness parameters, and the sample label B of the sample object information set B, and so on; the sample evaluation parameter value N of the business background parameters, the sample evaluation parameter value N of the channel parameters, the sample evaluation parameter value N of the timeliness parameters, the sample label N, and the prompt information of the sample object information set N are input into the large language model together, and the large language model is used to learn the data features of each of the multiple sample object information sets to obtain the credibility evaluation formula.

[0112] For example: Credibility evaluation formula = M*business background parameter + L*channel parameter + F*timeliness parameter. M, L, and F are weights respectively.

[0113] According to an embodiment of the present disclosure, the credibility evaluation formula is obtained by using a large language model, which can simplify processing operations and liberate manpower. In addition, the sample evaluation parameter values ​​and sample labels that have been processed are used to simplify the features to be learned in the large language model, reduce the processing difficulty of the large language model, and thus improve the accuracy of the credibility evaluation formula. According to another embodiment of the present disclosure, the credibility evaluation formula can also be obtained in the following manner: obtain the object information of the sample object from multiple different channels to obtain the object information of the sample object; based on each sample object information set, determine the sample evaluation parameter value and sample label of the sample evaluation parameter that matches the sample object information set, wherein the sample label represents the information credibility of the sample object information set; perform function fitting on multiple sample evaluation parameter values ​​and multiple sample labels to obtain the credibility evaluation formula.

[0114] According to the embodiments of the present disclosure, when the sample evaluation parameter values ​​and sample labels conform to the function distribution, a function fitting method can be used to perform function fitting on multiple sample evaluation parameter values ​​and multiple sample labels to obtain a credibility evaluation formula. The credibility evaluation formula conforms to the corresponding fitting function, and only the coefficients of the relevant evaluation parameters are the weights after fitting.

[0115] According to the embodiments of the present disclosure, the above-mentioned function fitting can be performed based on traditional statistical methods such as regression analysis, or based on machine learning methods such as decision trees, neural networks, support vector machines, Bayesian classifiers, random forests, clustering, principal component analysis, principal coordinate analysis and the like.

[0116] According to the embodiments of the present disclosure, a credibility evaluation formula is obtained by using function fitting, which is accurate and effective through data statistical analysis.

[0117] Figure 4 A schematic diagram of an object information set according to an embodiment of the present disclosure is schematically shown.

[0118] like Figure 4 As shown, the object information set may include basic information such as a mobile phone, information acquisition identification information such as an information source, and may also include information status, such as whether it is in use, whether it is online, whether it is valid, and so on.

[0119] like Figure 4 As shown, for object 1, multiple object information sets can be obtained from different information sources. The basic information of each object information set, such as mobile phone, is different. The business processing method provided by the embodiment of the present disclosure can be used to determine the formula information credibility of each object information set when handling pending business for object 1. Based on multiple formula information credibility, the target object information set is determined from multiple object information sets, and the pending business is handled based on the target object information set, so that the subsequent registration information is accurate and effective.

[0120] According to the embodiments of the present disclosure, the credibility of information is evaluated based on business background parameters, channel parameters, and timeliness parameters, and the business background, source channel, and timeliness of information updates that were originally relatively scattered can be taken into account as factors affecting the credibility evaluation, thereby improving the comprehensiveness of the analysis. By using the credibility evaluation formula to process the evaluation parameter values ​​and determine the information credibility of each object information set, the credibility evaluation process can be standardized, which improves the evaluation accuracy while also improving the processing efficiency, thereby ensuring the security and effectiveness of business processing.

[0121] Based on the above service processing method, the present disclosure also provides a service processing device. Figure 5 The device is described in detail.

[0122] Figure 5 The structural block diagram of the service processing device according to the embodiment of the present disclosure is schematically shown.

[0123] like Figure 5 As shown, the business processing device 500 of this embodiment includes an object information set generation module 510 , an evaluation parameter value determination module 520 , an information credibility determination module 530 , an information credibility determination module 540 and a business processing module 550 .

[0124] The object information set generation module 510 is used to obtain object information of a target object from multiple different channels to obtain multiple object information sets. In one embodiment, the object information set generation module 510 can be used to perform the operation S210 described above, which will not be described in detail here.

[0125] The evaluation parameter value determination module 520 is used to determine the evaluation parameter value of the evaluation parameter that matches the object information set. In one embodiment, the evaluation parameter value determination module 520 can be used to perform the operation S220 described above, which will not be described in detail here.

[0126] The information credibility determination module 530 is used to determine the information credibility of the object information set using the credibility evaluation formula and the evaluation parameter value to obtain multiple formula information credibility. In one embodiment, the information credibility determination module 530 can be used to perform the operation S230 described above, which will not be repeated here.

[0127] The target object information set determination module 540 is used to determine the target object information set from multiple object information sets based on multiple formula information credibility. In one embodiment, the target object information set determination module 540 can be used to perform the operation S240 described above, which will not be repeated here.

[0128] The business processing module 550 is used to process the pending business of the target object based on the target object information set. In one embodiment, the information credibility determination module 550 can be used to perform the operation S250 described above, which will not be described in detail here.

[0129] According to an embodiment of the present disclosure, the evaluation parameter value determination module 520 may include a business background parameter determination sub-module, and the business background parameter determination sub-module may include: a judgment unit, used to judge whether the object information set includes information for processing the business; a business identification information determination unit, used to determine the business identification information used to characterize the business type from the object information set; a business type information determination unit, used to determine the business type information of the business to be processed; a business background parameter evaluation parameter value determination unit, used to determine the evaluation parameter value of the business background parameter based on the business identification information and the business type information.

[0130] According to an embodiment of the present disclosure, the evaluation parameter value determination module 520 may further include a channel parameter determination submodule, which may include: an information generation identification information determination unit, used to determine the generation channel of the object information set; an information acquisition identification information determination unit, used to determine the acquisition channel of the object information set; a first channel parameter evaluation parameter value determination unit, used to determine the first channel parameter based on the information generation identification information and the predetermined generation standard information; a second channel parameter evaluation parameter value determination unit, used to determine the second channel parameter based on the information acquisition identification information and the predetermined acquisition standard information; a channel parameter evaluation parameter value determination unit, used to determine the evaluation parameter value of the first channel parameter and the evaluation parameter value of the second channel parameter.

[0131] According to an embodiment of the present disclosure, the evaluation parameter value determination module 520 may further include a timeliness parameter determination submodule, which may include: an information generation time determination unit, which determines the information generation time of the object information set; a validity period determination unit, which determines the validity period of the object information set; an information decay rate determination unit, which is used to determine the information decay rate based on the information generation time and the current time; a validity parameter value determination unit, which is used to determine the validity parameter value based on the validity period and the information generation time; and a timeliness parameter evaluation parameter value determination unit, which is used to obtain the evaluation parameter value of the timeliness parameter based on the information decay rate and the validity parameter value.

[0132] According to an embodiment of the present disclosure, an information credibility determination module may include a credibility evaluation formula determination submodule, including: a sample object information set acquisition unit, used to acquire object information of sample objects from multiple different channels to obtain multiple sample object information sets; a sample evaluation parameter value and sample label determination unit, used to determine, based on each sample object information set, sample evaluation parameter values ​​and sample labels of sample evaluation parameters that match the sample object information set, wherein the sample label characterizes the information credibility of the sample object information set; a credibility formula determination unit, used to input multiple sample evaluation parameter values, multiple sample labels and prompt information into a large language model to obtain a credibility evaluation formula, wherein the prompt information is used to instruct the large language model to perform a formula generation task and output expected results.

[0133] According to an embodiment of the present disclosure, the credibility evaluation formula determination submodule may further include a credibility formula determination unit for performing function fitting on multiple sample evaluation parameter values ​​and multiple sample labels to obtain a credibility evaluation formula.

[0134] According to an embodiment of the present disclosure, any multiple modules of the object information set generation module 510, the evaluation parameter value determination module 520, the information credibility determination module 530, the information credibility determination module 540 and the business processing module 550 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the object information set generation module 510, the evaluation parameter value determination module 520, the information credibility determination module 530, the information credibility determination module 540 and the business processing module 550 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 implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the object information set generation module 510, the evaluation parameter value determination module 520, the information credibility determination module 530, the information credibility determination module 540 and the business processing module 550 can be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function can be executed.

[0135] Figure 6 A block diagram of an electronic device suitable for implementing a service processing method according to an embodiment of the present disclosure is schematically shown.

[0136] like Figure 6 As shown, the electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 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.

[0137] In RAM 603, various programs and data required for the operation of electronic device 600 are stored. Processor 601, ROM 602 and RAM 603 are connected to each other via bus 604. Processor 601 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 602 and / or RAM 603. It should be noted that the program can also be stored in one or more memories other than ROM 602 and RAM 603. Processor 601 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in one or more memories.

[0138] According to an embodiment of the present disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the I / O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

[0139] 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.

[0140] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, 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, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0141] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the business processing method provided by the embodiment of the present disclosure.

[0142] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 601. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0143] 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 609, and / or installed from a removable medium 611. 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.

[0144] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by a computer program module.

[0145] 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).

[0146] The flow charts 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 flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order 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 flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0147] It will be appreciated by those skilled in the art that 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, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, 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 without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0148] 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 business processing method, characterized in that: The method comprises: Acquire object information of a target object from multiple different channels to obtain multiple object information sets; determining an evaluation parameter value of an evaluation parameter that matches the object information set; Determine the information credibility of the object information set by using a credibility evaluation formula and the evaluation parameter value, and obtain multiple formula information credibility, wherein the credibility evaluation formula includes the evaluation parameter and a weight matching the evaluation parameter; determining a target object information set from the plurality of object information sets based on the plurality of formula information credibility; and Perform business processing for the target object's pending business based on the target object information set.

2. The method according to claim 1, characterized in that: The evaluation parameters include business background parameters; The determining of the evaluation parameter value of the evaluation parameter matching the object information set comprises: In a case where it is determined that the object information set includes information for processing a service, determining service identification information for characterizing a service type from the object information set; Determining the business type information of the business to be processed; and Based on the service identification information and the service type information, an evaluation parameter value of the service background parameter is determined.

3. The method according to claim 1, characterized in that The evaluation parameters include channel parameters; The determining of the evaluation parameter value of the evaluation parameter matching the object information set comprises: Determine information generation identification information and information acquisition identification information from the object information set, wherein the information generation identification information represents a generation channel of the object information set, and the information acquisition identification information represents an acquisition channel of the object information set; Based on the evaluation parameter value of the first channel parameter and the evaluation parameter value of the second channel parameter, the evaluation parameter value of the channel parameter is determined, wherein the evaluation parameter value of the first channel parameter is determined based on the information generation identification information and the predetermined generation standard information, and the evaluation parameter value of the second channel parameter is determined based on the information acquisition identification information and the predetermined acquisition standard information.

4. The method according to claim 1, characterized in that The evaluation parameters include timeliness parameters; The determining of the evaluation parameter value of the evaluation parameter matching the object information set comprises: Determine the information generation time and validity period of the object information set; Determine an information decay rate based on the information generation time and the current time; determining a validity parameter value based on the validity period and the information generation time; and Based on the information decay rate and the validity parameter value, an evaluation parameter value of the timeliness parameter is obtained.

5. The method according to any one of claims 1 to 4, characterized in that The credibility evaluation formula is obtained in the following way: Acquire object information of sample objects from multiple different channels to obtain multiple sample object information sets; Based on each of the sample object information sets, determining a sample evaluation parameter value and a sample label of a sample evaluation parameter that matches the sample object information set, wherein the sample label represents the information credibility of the sample object information set; and Inputting the plurality of sample evaluation parameter values, the plurality of sample labels and prompt information into a large language model to obtain the credibility evaluation formula, wherein the prompt information is used to instruct the large language model to execute a formula generation task and output an expected result.

6. The method according to any one of claims 1 to 4, characterized in that The credibility evaluation formula is obtained in the following way: Acquire object information of sample objects from multiple different channels to obtain an object information set of the sample objects; Based on each of the sample object information sets, determining a sample evaluation parameter value and a sample label of a sample evaluation parameter that matches the sample object information set, wherein the sample label represents the information credibility of the sample object information set; as well as Function fitting is performed on the multiple sample evaluation parameter values ​​and the multiple sample labels to obtain the credibility evaluation formula.

7. The method according to any one of claims 1 to 4, characterized in that The using the credibility evaluation formula and the evaluation parameter value to determine the credibility of the formula information of each object information set includes: Based on the evaluation parameter, the evaluation parameter value is substituted into the credibility evaluation formula to obtain the formula information credibility of each of the object information sets.

8. A service processing device, characterized in that: The device comprises: An object information set generation module is used to obtain object information of a target object from multiple different channels to obtain multiple object information sets; An evaluation parameter value determination module, used to determine the evaluation parameter value of the evaluation parameter matching the object information set; An information credibility determination module, used to determine the information credibility of the object information set by using a credibility evaluation formula and the evaluation parameter value, and obtain multiple formula information credibility, wherein the credibility evaluation formula includes the evaluation parameter and a weight matching the evaluation parameter; a target object information set determination module, configured to determine a target object information set from the plurality of object information sets based on the plurality of formula information credibility; and A business processing module is used to perform business processing for the target object's pending business based on the target object information set.

9. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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