CN116451938A

CN116451938A - Task processing method and device, electronic equipment and storage medium

Info

Publication number: CN116451938A
Application number: CN202310274349.3A
Authority: CN
Inventors: 赵傲; 俞栋辉; 姜嘉祺; 蒋馨剑
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2023-03-21
Filing date: 2023-03-21
Publication date: 2023-07-18

Abstract

The disclosure provides a task processing method, a task processing device, electronic equipment and a storage medium, which can be applied to the technical field of finance and the technical field of computers. The method comprises the following steps: responding to a distribution task related to financial business in a financial platform, and acquiring first target historical data related to a target user and second target historical data related to a salesman, wherein the first target historical data and the second target historical data are acquired through the authorization of the salesman; determining a target index value corresponding to the target index according to the first target history data and the second target history data; determining the matching degree between the target user and the salesman by utilizing a pre-trained matching model based on the target index value; and distributing the financial business to the target salesman according to a preset distribution rule based on the matching degree.

Description

Task processing method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of financial technology and the field of computer technology, and in particular, to a task processing method, apparatus, electronic device, storage medium, and program product.

Background

In order to promote the business volume, the financial institution issues financial business to the business staff, so that the business staff can develop business to the user according to the financial business. However, due to the fact that personal abilities of the operators are not balanced, potential values of users are not balanced, and the like, the service volume of the financial institutions cannot be improved well.

In the process of implementing the disclosed concept, the inventor finds that at least the following problems exist in the related art: in the related art, when a financial institution distributes financial services to a salesman, the efficiency is low, the labor cost is high, and the financial services cannot be distributed to the salesman reasonably.

Disclosure of Invention

In view of the above, the present disclosure provides a task processing method, apparatus, electronic device, storage medium, and program product.

According to a first aspect of the present disclosure, there is provided a task processing method, including:

responding to a distribution task related to financial business in a financial platform, and acquiring first target historical data related to a target user and second target historical data related to a salesman, wherein the first target historical data and the second target historical data are acquired through the authorization of the salesman;

determining a target index value corresponding to the target index according to the first target history data and the second target history data;

Determining the matching degree between the target user and the salesman by utilizing a pre-trained matching model based on the target index value;

and distributing the financial business to the target salesman according to a preset distribution rule based on the matching degree.

According to an embodiment of the present disclosure, determining a degree of matching between a target user and a salesman using a pre-trained matching model based on target index values includes:

normalizing the target index value based on the index value type of the target index value to obtain a target index processing value;

and determining the matching degree between the target user and the salesman by utilizing a pre-trained matching model based on the target index processing value.

According to an embodiment of the present disclosure, distributing a financial service to a target salesman according to a preset distribution rule based on a matching degree, including:

determining target matching degree based on the matching degree between the salesmen and the target user, wherein the target matching degree represents the largest matching degree in the matching degrees;

determining a target distribution mode from a plurality of distribution modes according to the target matching degree;

and distributing the financial business to a target salesman according to the target distribution mode.

According to an embodiment of the present disclosure, the target salesman includes a first class of salesman and a second class of salesman, where the second class of salesman is a manager of the first class of salesman, and determining, according to the target matching degree, a target allocation manner from a plurality of allocation manners includes:

Comparing the target matching degree with a first preset threshold value to obtain a first comparison result;

under the condition that the first comparison result is determined to indicate that the target matching degree is larger than a first preset threshold value, determining that a target distribution mode is a first distribution mode, wherein the first distribution mode indicates that financial services are distributed to first class operators;

and under the condition that the first comparison result represents that the target matching degree is smaller than or equal to a first preset threshold value, determining that the target distribution mode is a second distribution mode, wherein the second distribution mode represents that the financial business is distributed to a second class of operators.

According to an embodiment of the disclosure, the first allocation method includes a first sub-allocation method and a second sub-allocation method, and the first class of operators includes a first sub-class of operators and a second sub-class of operators;

under the condition that the first comparison result is determined to indicate that the target matching degree is larger than a first preset threshold, determining that the target allocation mode is a first allocation mode comprises the following steps:

comparing the target matching degree with a second preset threshold value under the condition that the first comparison result is determined to represent that the target matching degree is larger than the first preset threshold value, and determining a second comparison result;

under the condition that the second comparison result represents that the target matching degree is larger than or equal to a second preset threshold value, determining that the target allocation mode is a first sub-allocation mode, wherein the first sub-allocation mode represents that the financial service is allocated to a first sub-class salesman, and the first sub-class salesman represents a salesman corresponding to the target matching degree;

And under the condition that the second comparison result represents that the target matching degree is smaller than a second preset threshold value, determining that the target allocation mode is a second sub-allocation mode, wherein the second sub-allocation mode represents that the financial service is randomly allocated to a second sub-class of operators, and the second sub-class of operators represent operators corresponding to the matching degree which is larger than the first preset threshold value and smaller than the second preset threshold value.

According to an embodiment of the present disclosure, distributing financial services to a target salesman in a target distribution manner includes:

under the condition that the target matching degree is larger than or equal to a first preset threshold value, distributing the financial service to a first sub-class salesman according to a first sub-distribution mode;

under the condition that the target matching degree is smaller than a first preset threshold value and larger than a second preset threshold value, distributing the financial service to a second sub-class salesman according to a second sub-distribution mode;

and distributing the financial service to the second class of operators according to a second distribution mode under the condition that the target matching degree is less than or equal to a second preset threshold value.

According to an embodiment of the present disclosure, a training method of a matching model includes:

acquiring initial sample data, wherein the initial sample data characterizes financial data generated by users and operators in a historical time period;

Preprocessing initial sample data according to a preset processing rule to obtain target sample data, wherein the target sample data comprises a history matching degree between a user and a salesman and a target history index value of a target index;

and training the initial matching model by taking the target history index value as input and the history matching degree as output to obtain a matching model.

According to an embodiment of the present disclosure, preprocessing initial sample data according to a preset processing rule to obtain target sample data includes:

screening the initial sample data to obtain first sub-target sample data;

determining a sample type of the first sub-target sample data according to the financial transaction record in the first sub-target sample data;

according to the first sub-target sample type, determining the history matching degree between the user and the service personnel;

according to the index type of the target index in the first sub-target sample data, carrying out normalization processing on the initial historical index value of the target index to obtain a target historical index value;

and determining target sample data based on the history matching degree and the target history index value.

A second aspect of the present disclosure provides a task processing device, including:

The acquisition module is used for responding to the distribution task about the financial business in the financial platform and acquiring first target historical data related to the target user and second target historical data related to the salesman, wherein the first target historical data and the second target historical data are acquired through the authorization of the salesman;

the first determining module is used for determining a target index value corresponding to the target index according to the first target historical data and the second target historical data;

the second determining module is used for determining the matching degree between the target user and the salesman by utilizing a pre-trained matching model based on the target index value;

and the distribution module is used for distributing the financial business to the target salesman according to a preset distribution rule based on the matching degree.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method described above.

A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described method.

A fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above method.

According to the task processing method, the task processing device, the task processing equipment, the task processing medium and the task processing program product, the target index value is determined according to the first target historical data of the target user and the second target historical data of the salesman by acquiring the first target historical data of the target user and the second target historical data of the salesman, and the matching degree between the target user and the salesman is determined by the index value through the matching model, so that the financial business can be distributed to the target salesman according to the matching degree and a preset distribution rule, the first target historical data of the target user and the second target historical data of the salesman are fully utilized, automatic distribution of the financial business to the target salesman is realized, and the labor consumption is reduced. Therefore, the technical problems that in the related art, when a financial institution distributes financial services to a salesman, the efficiency is lower, the financial services cannot be reasonably distributed to the salesman, and the labor cost is higher are at least partially solved.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an application scenario diagram of a task processing method, apparatus, device, medium and program product according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a task processing method according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow chart of a training method of a matching model according to an embodiment of the disclosure;

FIG. 4 schematically illustrates a network architecture schematic of a matching model according to an embodiment of the present disclosure;

FIG. 5 schematically illustrates a block diagram of a task processing device according to an embodiment of the present disclosure; and

fig. 6 schematically illustrates a block diagram of an electronic device adapted to implement a task processing method according to an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

In the technical scheme of the disclosure, the related data (such as including but not limited to personal information of a user) are collected, stored, used, processed, transmitted, provided, disclosed, applied and the like, all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public welcome is not violated.

Currently, the allocation of financial tasks to a financial institution about a target user still depends on the modes of active claim, autonomous allocation of management personnel, random allocation of a system and the like. However, the current situation that the personal ability of the service personnel is unbalanced, the potential value of the target client is unbalanced, and the data and the like which are generated by the user and the service personnel cannot be fully utilized exists. The quality of financial business is optimal and the conversion result is optimal through irregular manual distribution or random distribution, repeated distribution also causes waste of manpower, user experience is reduced, and improper task distribution also causes risk of loss of high-quality users.

In view of this, an embodiment of the present disclosure provides a task processing method, including: responding to a distribution task related to financial business in a financial platform, and acquiring first target historical data related to a target user and second target historical data related to a salesman, wherein the first target historical data and the second target historical data are acquired through the authorization of the salesman; determining a target index value corresponding to the target index according to the first target history data and the second target history data; determining the matching degree between the target user and the salesman by utilizing a pre-trained matching model based on the target index value; and distributing the financial business to the target salesman according to a preset distribution rule based on the matching degree.

Fig. 1 schematically illustrates an application scenario diagram of a task processing method, apparatus, device, medium and program product according to an embodiment of the present disclosure.

As shown in fig. 1, an application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 through the network 104 using at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103, to receive or send messages, etc. Various communication client applications, such as a shopping class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only) may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103.

The first terminal device 101, the second terminal device 102, the third terminal device 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by the user using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that, the task processing method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the task processing devices provided by the embodiments of the present disclosure may be generally disposed in the server 105. The task processing method provided by the embodiment of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105. Accordingly, the task processing device provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The task processing method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 4 based on the scenario described in fig. 1.

Fig. 2 schematically illustrates a flow chart of a task processing method according to an embodiment of the present disclosure.

As shown in fig. 2, the task processing method of this embodiment includes operations S210 to S240.

In operation S210, first target history data related to a target user and second target history data related to a salesman, which are acquired through the authority of the salesman, are acquired in response to an allocation task for financial services in the financial platform.

According to embodiments of the present disclosure, the financial platform may be a platform developed by a financial institution for a salesman to conduct financial transactions. The financial transaction may be, but is not limited to, a loan transaction, a withdrawal transaction, a fund transaction, or a stock transaction.

According to an embodiment of the present disclosure, the assignment task may be a task of assigning a financial service to a salesman so that the salesman can develop the financial service to a target user. The assignment task may be associated with the target user.

According to an embodiment of the present disclosure, the target user may be a user who has screened out a need to develop a financial service in advance.

According to embodiments of the present disclosure, the business person may be a staff member of a financial institution. For example, the salesman may be a customer manager of a financial institution.

According to an embodiment of the present disclosure, the first target history data may be data that a target user has occurred within a predetermined history period. For example, it may be the loan data of the user. The second target history data may be data that a salesman has occurred within a predetermined history period. For example, the second target history data may be generated from business transacted by the business person for the customer during the financial institution's work. The predetermined historical time period may be one, two or three years.

In operation S220, a target index value corresponding to the target index is determined according to the first target history data and the second target history data.

According to embodiments of the present disclosure, the target metrics may include a marketing capability metric, a user preference metric, or other metrics, for example, the target metrics may be an industry match metric, a user personality metric, a distance metric, or an existing business metric, it being understood that the present disclosure is not limited thereto.

According to the embodiment of the disclosure, the target index value corresponding to the target index can be obtained by screening the first target historical data and the second target historical data according to the target index.

In operation S230, a degree of matching between the target user and the salesman is determined using a pre-trained matching model based on the target index value.

According to embodiments of the present disclosure, the matching model may be a pre-trained model that is capable of outputting a degree of matching between the user and the salesman.

According to embodiments of the present disclosure, the degree of matching may characterize the likelihood of success of a target user in transacting a financial transaction in the event that a salesman is conducting the financial transaction to the target user. The larger the matching degree is, the more likely the target user transacts financial business, and the smaller the matching degree is, the less likely the target user transacts financial business.

According to the embodiments of the present disclosure, since the target index value is an index value related to the target user and the salesman, inputting the target index value to the matching model can output the degree of matching between the target user and the salesman.

In operation S240, the financial service is distributed to the target salesman according to a preset distribution rule based on the matching degree.

According to an embodiment of the present disclosure, the preset allocation rule may be a rule for allocating according to a matching degree order, for example, the operators corresponding to the matching degree are arranged according to a book order of the matching degree from large to small, and the operator arranged first is determined as a target operator, so that the financial service is allocated to the target operator, so that the target operator performs the financial service to the target user.

According to the embodiment of the disclosure, the first target historical data of the target user and the second target historical data of the salesman are obtained, the target index value is determined according to the first target historical data and the second target historical data, and the matching degree between the target user and the salesman is determined by the index value through the matching model, so that the financial business can be distributed to the target salesman according to the matching degree and a preset distribution rule, the first target historical data of the target user and the second target historical data of the salesman are fully utilized, automatic distribution of the financial business to the target salesman is realized, and manual consumption is reduced. Therefore, the technical problems that in the related art, when a financial institution distributes financial services to a salesman, the efficiency is lower, the financial services cannot be reasonably distributed to the salesman, and the labor cost is higher are at least partially solved.

In order to better understand the embodiments of the present disclosure, a training method of the matching model in the embodiments of the present disclosure is described below with reference to fig. 3 and 4.

Fig. 3 schematically illustrates a flow chart of a training method of a matching model according to an embodiment of the present disclosure.

As shown in fig. 3, the training method includes operations S310 to S330.

In operation S310, initial sample data is acquired, wherein the initial sample data characterizes financial data generated by users and operators over a historical period of time.

In operation S320, the initial sample data is preprocessed according to a preset processing rule, so as to obtain target sample data, where the target sample data includes a history matching degree between the user and the salesman and a target history index value of the target index.

In operation S330, the initial matching model is trained with the target history index value as input and the history matching degree as output, to obtain a matching model.

According to embodiments of the present disclosure, the initial sample data may be stored in a database of the financial institution.

According to the embodiment of the disclosure, the problem that data is missing, too large or too small may exist in the initial sample data, so a preset processing rule may be set, and the initial sample data is preprocessed according to the preset processing rule to obtain the target sample data. For example, the financial transaction records in the initial sample data may be supplemented with data missing.

According to embodiments of the present disclosure, a target historical index value of a target index over a historical period of time may be determined from initial sample data.

According to an embodiment of the present disclosure, the target history matching degree may be determined based on a business conversion rate between the user and the business person, wherein the business conversion rate refers to a financial amount generated by the business person who performs a financial business to the user, and the user successfully transacts the financial business.

According to the embodiment of the disclosure, the target history index value is used as input, the history matching degree is used as output to train the initial matching model, and the initial matching model is converged to obtain the matching model.

Fig. 4 schematically illustrates a network structure diagram of a matching model according to an embodiment of the present disclosure.

As shown in fig. 4, the network structure of the matching model may include an input layer L1, a hidden layer L2, a hidden layer L3, a hidden layer L4, and an output layer L5. The target history index value input by the input layer L1 may be 11, that is, 11 total neuron nodes of the input layer L1, the hidden layer L2 may include 20 neuron nodes, the hidden layer L3 may include 50 neuron nodes, the hidden layer L4 may include 20 neuron nodes, and the output layer L5 may include 1 neuron node, that is, output history matching degree. It will be appreciated that the number of neuron nodes and the number of hidden layers for each layer of the matching model are merely illustrative and not limiting.

Further, the output layer may use Tanh as an activation function to keep the output data range within (-1, 1), corresponding to the history matching degree. The connection between the network layers is as in formula (1):

L _(n+1 )＝L _n *W _n +b _n (1)

wherein L is _n Represents a layer n network structure, W _n Representing a weight matrix, b _n Representing the bias matrix.

For the calculation result of each layer, an ELU function may be used as an activation function, the ELU function using formula (2):

where x represents the calculation result of each layer and α represents the first parameter.

Instead of using an ELU function for the activation function at the output layer L5, a Tanh function may be used as the activation function, as shown in the following equation (3):

wherein x is ₁ The calculation result of the output layer L5 is shown.

screening the initial sample data to obtain first sub-target sample data;

According to the embodiment of the disclosure, according to the dimension of the salesman, deleting the data of which the financial transaction records of the salesman are smaller than the preset threshold number to obtain the first sub-target sample data.

According to the embodiment of the disclosure, the first sub-target sample data can be divided into different types according to the financial amount of the user and the bad amount in the financial transaction record, wherein the bad amount refers to the amount that the user does not fulfill on schedule after the financial institution transacts the financial business.

According to an embodiment of the present disclosure, the type of the first sub-target sample data may be a high-quality type, a low-quality type, and an invalid type, wherein the high-quality type may be a financial transaction record having a financial amount greater than 0 and a bad amount equal to 0, the low-quality type may be a financial transaction record having a financial amount greater than 0 and a bad amount greater than 0, and the invalid type may be a financial transaction record having a financial amount equal to 0.

According to an embodiment of the present disclosure, the target history matching degree corresponding to the high-quality type financial transaction record may be a proportion of the financial amount of the user in the financial institution to all users, that is, a value range of (0, 1), the target history matching degree corresponding to the low-quality type financial transaction record may be a proportion of the negative user bad amount to the financial amount, that is, a value range of [ -1, 0), and the target history matching degree corresponding to the invalid type financial transaction record may be 0.

According to the embodiment of the disclosure, in order to ensure the authenticity of the sample, the proportion of the first sub-target sample data of the quality type, the bad type and the invalid type can be controlled to be 3:1:5.

According to the embodiment of the disclosure, since the initial history index values of the target indexes are different, the initial history index values can be normalized to obtain the target history index values.

According to the embodiment of the disclosure, the information of the user and the information of the salesman are fully combined through the pre-training model, so that the efficiency of the financial institution in distributing financial services is improved, the service conversion rate of the financial institution is further improved, and the resource distribution is optimized.

According to an embodiment of the present disclosure, the index value type of the target index value may be a percentage value type or a dictionary value type,

According to embodiments of the present disclosure, the percentage value may be determined based on the percentage of different data in the financial transaction records that have occurred to the total financial transaction record. For example, the index value type of the target index value of the industry matching degree index may be a percentage value type, all financial transaction records of a business person in the industry may be obtained as denominators, and the number of financial transaction records in which the business person successfully transacts business to the user in the financial transaction records is used as a numerator to obtain the target index value of the industry matching degree index. It is to be understood that the present disclosure is not limited to this manner of value.

According to an embodiment of the present disclosure, the dictionary value type may be determined according to a section in which the target index value is located or with a preset dictionary value. For example, the index value type of the target index value of the distance index may be dictionary valued, the distance between the business person and the target user may be acquired, the target value of the distance index may be determined to be 1 in a first section (0-10 km), the target index value of the distance index may be determined to be 2 in a second section (10-20 km), the target value of the distance index may be determined to be 3 in a third section (20-30 km), and the target index value may be normalized according to the number of financial transaction records in each section to obtain a target index processing value.

According to the embodiment of the disclosure, the target index value is obtained by carrying out normalization processing on the target index value, so that the values obtained by the matching model are more uniform, and the calculation efficiency of the matching model is improved.

According to the embodiment of the disclosure, the matching degree can be set into a plurality of intervals, each interval corresponds to a different allocation mode, and the target allocation mode is determined according to the interval where the target matching degree is located.

According to the embodiment of the disclosure, the allocation manner may be random allocation, allocation according to a sequence, allocation according to a distance between a service person and a user, or allocation according to current traffic of the service person.

According to the embodiment of the disclosure, the financial business can be automatically distributed to the target operators according to the target distribution mode, and the target operators are obtained according to the matching, so that the financial business does not need to be manually distributed, and the labor cost is reduced.

According to the embodiment of the disclosure, when the target matching degree is larger than the first preset threshold value, the risk of the target user is smaller, and financial services can be distributed to first class operators.

According to embodiments of the present disclosure, the first class of operators may be general operators and the second class of operators may be higher-level or more experienced operators than the first class of operators.

According to the embodiment of the disclosure, the first preset threshold may be set to 0.2, and the target matching degree being smaller than or equal to the first preset threshold indicates that the target user may have a risk, so that the financial service may be directly distributed to the second class of operators, and the second class of operators determines whether to develop the financial service. If the financial business is developed, the financial business can be distributed to the first class of operators according to the target matching degree, and if the financial business is not developed, the financial business is not required to be distributed.

According to the embodiment of the disclosure, different distribution modes are determined according to the matching degree, so that the probability of successful transaction of the financial business can be enhanced on the basis of reducing the risk in the process of developing the financial business.

According to the embodiment of the disclosure, the first sub-class service staff can distribute the financial service to the first sub-class service staff according to the first sub-distribution mode, namely, the service staff with the highest matching degree with the target user under the condition that the second comparison result is determined to represent that the target matching degree is greater than or equal to the second preset threshold value, so that the potential maximum value can be brought to the financial institution.

According to the embodiment of the disclosure, under the condition that a plurality of first sub-class operators exist, that is, the target matching degree of the plurality of operators is the same, the first sub-class operators which are closest to the target user can be allocated to the first sub-class operators with the smallest current traffic if the plurality of first sub-class operators with the same distance exist, and random allocation is performed if the plurality of first sub-class operators meet the condition. The second preset threshold may be set to 0.6.

According to the embodiment of the disclosure, in the case that the second comparison result is determined to indicate that the target matching degree is smaller than the second preset threshold, the financial tasks may be distributed in a second sub-distribution manner. Firstly, determining a second sub-class salesman corresponding to the matching degree smaller than a second preset threshold and larger than the first preset threshold, and randomly distributing the financial business to the second sub-class salesman.

According to the embodiment of the disclosure, different allocation modes can avoid that the financial services allocated by the operators each time are single or no financial services can be allocated, can also prevent the quantity and quality of the financial services allocated by different operators from generating larger deviation, and can further avoid the problem of over fitting when the matching model is trained again according to the current data.

Based on the task processing method, the disclosure also provides a task processing device. The device will be described in detail below in connection with fig. 5.

Fig. 5 schematically shows a block diagram of a task processing device according to an embodiment of the present disclosure.

As shown in fig. 5, the task processing device 500 of this embodiment includes an acquisition module 510, a first determination module 520, a second determination module 530, and an allocation module 540.

The obtaining module 510 is configured to obtain, in response to a task associated with a financial transaction in the financial platform, first target historical data related to a target user and second target historical data related to a salesman, where the first target historical data and the second target historical data are obtained through authorized collection by the salesman. In an embodiment, the obtaining module 510 may be configured to perform the operation S210 described above, which is not described herein.

The first determining module 520 is configured to determine a target index value corresponding to the target index according to the first target history data and the second target history data. In an embodiment, the first determining module 520 may be configured to perform the operation S220 described above, which is not described herein.

The second determining module 530 is configured to determine, based on the target index value, a degree of matching between the target user and the salesman using a pre-trained matching model, and in an embodiment, the second determining module 530 may be configured to perform the operation S230 described above, which is not described herein.

The allocation module 540 is configured to allocate the financial service to the target salesman according to a preset allocation rule based on the matching degree. In an embodiment, the allocation module 540 may be configured to perform the operation S240 described above, which is not described herein.

According to an embodiment of the present disclosure, the second determining module 530 for determining a degree of matching between the target user and the salesman using a pre-trained matching model based on the target index value includes:

the first determining submodule is used for carrying out normalization processing on the target index value based on the index value type of the target index value to obtain a target index processing value;

And the second determining submodule is used for determining the matching degree between the target user and the salesman by utilizing a pre-trained matching model based on the target index processing value.

According to an embodiment of the present disclosure, the allocation module 540 for allocating a financial service to a target salesman according to a preset allocation rule based on a matching degree includes:

the first distribution sub-module is used for determining target matching degree based on the matching degree between the salesman and the target user, wherein the target matching degree represents the largest matching degree in the matching degrees;

the second allocation sub-module is used for determining a target allocation mode from a plurality of allocation modes according to the target matching degree;

and the third distribution sub-module is used for distributing the financial business to the target salesman according to the target distribution mode.

According to an embodiment of the present disclosure, the target salesman includes a first class of salesman and a second class of salesman, where the second allocation submodule for determining a target allocation manner from a plurality of allocation manners according to a target matching degree includes:

the first distribution unit is used for comparing the target matching degree with a first preset threshold value to obtain a first comparison result;

The second allocation unit is used for determining that the target allocation mode is a first allocation mode under the condition that the first comparison result indicates that the target matching degree is larger than a first preset threshold, wherein the first allocation mode indicates that the financial service is allocated to a first class of operators;

and the third distribution unit is used for determining that the target distribution mode is a second distribution mode under the condition that the first comparison result represents that the target matching degree is smaller than or equal to a first preset threshold value, wherein the second distribution mode represents that the financial business is distributed to a second class of operators.

the second allocation unit for determining that the target allocation mode is the first allocation mode when determining that the first comparison result indicates that the target matching degree is greater than the first preset threshold value comprises:

the first distribution subunit is used for comparing the target matching degree with a second preset threshold value and determining a second comparison result under the condition that the first comparison result indicates that the target matching degree is larger than the first preset threshold value;

The second allocation subunit is configured to determine, when it is determined that the second comparison result indicates that the target matching degree is greater than or equal to a second preset threshold, that the target allocation mode is a first sub-allocation mode, where the first sub-allocation mode indicates that the financial service is allocated to a first sub-class of operators, and the first sub-class of operators indicates that the operators corresponding to the target matching degree;

and the third allocation subunit is used for determining that the target allocation mode is a second sub-allocation mode under the condition that the second comparison result indicates that the target matching degree is smaller than a second preset threshold, wherein the second sub-allocation mode indicates that the financial service is randomly allocated to a second sub-class of operators, and the second sub-class of operators indicates operators corresponding to the matching degree which is larger than the first preset threshold and smaller than the second preset threshold.

According to an embodiment of the present disclosure, a third distribution sub-module for distributing financial transactions to a target salesman in a target distribution manner includes:

the fourth distribution unit is used for distributing the financial service to the first sub-class operators according to the first sub-distribution mode under the condition that the target matching degree is determined to be greater than or equal to a first preset threshold value;

a fifth allocation unit, configured to allocate the financial service to a second sub-class attendant according to a second sub-allocation manner when it is determined that the target matching degree is smaller than the first preset threshold and greater than the second preset threshold;

And the sixth allocation unit is used for allocating the financial business to the second class of operators according to the second allocation mode under the condition that the target matching degree is less than or equal to a second preset threshold value.

According to an embodiment of the present disclosure, the training method of the matching model in the task processing device includes:

According to an embodiment of the present disclosure, in the training method of the matching model in the task processing device, preprocessing initial sample data according to a preset processing rule to obtain target sample data, including:

screening the initial sample data to obtain first sub-target sample data;

and determining target sample data according to the history matching degree and the target history index value.

According to an embodiment of the present disclosure, any of the plurality of modules of the acquisition module 510, the first determination module 520, the second determination module 530, and the allocation module 540 may be combined in one module to be implemented, or any of the plurality of modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the acquisition module 510, the first determination module 520, the second determination module 530, and the allocation module 540 may be implemented at least in part as hardware circuitry, 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 may be implemented in hardware or firmware in any other reasonable way of integrating or packaging the circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the acquisition module 510, the first determination module 520, the second determination module 530, and the allocation module 540 may be at least partially implemented as a computer program module, which when executed, may perform the respective functions.

As shown in fig. 6, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601 that 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 section 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 an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.

In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 602 and/or the RAM 603. Note that the program may be stored in one or more memories other than the ROM 602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, the electronic device 600 may also include an input/output (I/O) interface 605, the input/output (I/O) interface 605 also being connected to the bus 604. The electronic device 600 may also include one or more of the following components connected to an input/output (I/O) interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to an input/output (I/O) interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 602 and/or RAM 603 and/or one or more memories other than ROM 602 and RAM 603 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code means for causing a computer system to carry out the task processing methods provided by the embodiments of the present disclosure when the computer program product is run on the computer system.

The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and/or installed from the removable medium 611. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only 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 embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims

1. A task processing method, comprising:

Determining a target index value corresponding to a target index according to the first target history data and the second target history data;

and distributing the financial business to a target salesman according to a preset distribution rule based on the matching degree.

2. The method of claim 1, wherein the determining a degree of match between the target user and the salesman using a pre-trained matching model based on the target index values comprises:

3. The method of claim 1, wherein the distributing the financial transaction to the target salesman according to a preset distribution rule based on the matching degree comprises:

determining a target matching degree based on the matching degree between the salesman and the target user, wherein the target matching degree represents the largest matching degree in the matching degrees;

4. The method of claim 3, wherein the target operators include a first type of operators and a second type of operators, the second type of operators being administrators of the first type of operators, wherein the determining a target allocation from a plurality of allocation based on the target match comprises:

determining the target allocation mode as a first allocation mode under the condition that the first comparison result represents that the target matching degree is larger than the first preset threshold, wherein the first allocation mode represents that the financial service is allocated to the first class of operators;

and under the condition that the first comparison result represents that the target matching degree is smaller than or equal to the first preset threshold value, determining that the target distribution mode is a second distribution mode, wherein the second distribution mode represents that the financial service is distributed to the second class of service staff.

5. The method of claim 4, wherein the first allocation pattern comprises a first sub-allocation pattern and a second sub-allocation pattern, the first class of operators comprising a first sub-class of operators and a second sub-class of operators;

wherein, when determining that the first comparison result indicates that the target matching degree is greater than the first preset threshold, determining that the target allocation mode is a first allocation mode includes:

determining the target allocation mode as the first sub-allocation mode under the condition that the second comparison result indicates that the target matching degree is larger than or equal to the second preset threshold value, wherein the first sub-allocation mode indicates that the financial service is allocated to the first sub-class salesman, and the first sub-class salesman indicates a salesman corresponding to the target matching degree;

and under the condition that the second comparison result represents that the target matching degree is smaller than the second preset threshold value, determining that the target allocation mode is the second sub-allocation mode, wherein the second sub-allocation mode represents that the financial service is randomly allocated to the second sub-class salesman, and the second sub-class salesman represents a salesman corresponding to the matching degree which is larger than the first preset threshold value and smaller than the second preset threshold value.

6. The method of claim 5, wherein said distributing the financial transaction to a target salesman in the target distribution manner comprises:

under the condition that the target matching degree is larger than or equal to a first preset threshold value, distributing the financial service to the first sub-class operators according to a first sub-distribution mode;

under the condition that the target matching degree is smaller than the first preset threshold value and larger than a second preset threshold value, distributing the financial business to the second sub-class business staff according to a second sub-distribution mode;

and distributing the financial service to the second class of operators according to a second distribution mode under the condition that the target matching degree is smaller than or equal to the second preset threshold value.

7. The method of any of claims 1 to 6, wherein the training method of the matching model comprises:

acquiring initial sample data, wherein the initial sample data characterizes financial data generated by a user and the salesman in a historical time period;

preprocessing the initial sample data according to a preset processing rule to obtain target sample data, wherein the target sample data comprises a history matching degree between the user and the salesman and a target history index value of the target index;

And training an initial matching model by taking the target history index value as input and the history matching degree as output to obtain the matching model.

8. The method of claim 7, wherein the preprocessing the initial sample data according to a preset processing rule to obtain target sample data comprises:

screening the initial sample data to obtain first sub-target sample data;

determining the history matching degree between the user and the salesman according to the first sub-target sample type;

normalizing the initial historical index value of the target index according to the index type of the target index in the first sub-target sample data to obtain the target historical index value;

and determining the target sample data based on the history matching degree and the target history index value.

9. A task processing device comprising:

the acquisition module is used for responding to the distribution task about the financial business in the financial platform and acquiring first target historical data related to a target user and second target historical data related to a salesman, wherein the first target historical data and the second target historical data are acquired through the authorization of the salesman;

The first determining module is used for determining a target index value corresponding to a target index according to the first target historical data and the second target historical data;

and the distribution module is used for distributing the financial business to a target salesman according to a preset distribution rule based on the matching degree.

10. An electronic device, comprising:

one or more processors;

a memory for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-8.

11. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-8.

12. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 8.