CN116992085A

CN116992085A - Data processing method and device

Info

Publication number: CN116992085A
Application number: CN202210446497.4A
Authority: CN
Inventors: 雷耀强; 蒋宁; 罗仕杰; 赵飞; 罗宁; 姜永峰
Original assignee: Mashang Consumer Finance Co Ltd
Current assignee: Mashang Consumer Finance Co Ltd
Priority date: 2022-04-26
Filing date: 2022-04-26
Publication date: 2023-11-03

Abstract

The application discloses a data processing method and a device, wherein the method comprises the following steps: acquiring target user appeal, wherein the target user appeal carries a target user identifier; determining a target scene corresponding to the target user appeal from a plurality of scenes; determining target strategy paths which are respectively matched with target user information and target user requirements from a target strategy brain graph corresponding to the target scene, wherein the target user information is determined based on the target user identification; and rendering a target brain graph based on the target strategy path, wherein the target brain graph is used for providing a path for processing a strategy required by the target user. The embodiment of the application can improve the data processing efficiency.

Description

Data processing method and device

Technical Field

The present application relates to the field of computer technologies, and in particular, to a data processing method and apparatus.

Background

At present, processing schemes related to user requirements are usually displayed in the form of knowledge documents, and when an agent handles the user requirements, the agent needs to query the corresponding processing schemes from the related knowledge documents. This can cause problems of time consuming inquiry, easy error, inability to monitor, difficult data statistics, etc. when the agent handles the user's appeal. In order to quickly and accurately process the user appeal, the prior art teases out the complaint points and the corresponding processing strategies in a series of user appeal in the form of brain graphs, so that the agent can quickly determine the processing strategies of the complaint points in the user appeal based on the brain graphs.

The existing brain map scheme for processing the user appeal usually displays the complete brain map, and the complete brain map has the problems of low retrieval efficiency aiming at the complaint points due to the multiple branches and large data volume, so that the problem of low processing efficiency of the user appeal is caused.

Disclosure of Invention

The embodiment of the application aims to provide a data processing method and device which are used for improving data processing efficiency.

In a first aspect, a data processing method is provided, including:

acquiring target user appeal, wherein the target user appeal carries a target user identifier;

determining a target scene corresponding to the target user appeal from a plurality of scenes;

determining target strategy paths which are respectively matched with target user information and target user requirements from a target strategy brain graph corresponding to the target scene, wherein the target user information is determined based on the target user identification;

and rendering a target brain graph based on the target strategy path, wherein the target brain graph is used for providing a path for processing a strategy required by the target user.

In a second aspect, there is provided a data processing apparatus comprising:

the system comprises a appeal acquisition unit, a target user appeal acquisition unit and a target user appeal acquisition unit, wherein the target user appeal carries a target user identifier;

A scene determining unit, configured to determine a target scene corresponding to the target user appeal from a plurality of scenes;

the path determining unit is used for determining target strategy paths which are respectively matched with target user information and the target user demands from target strategy brain diagrams corresponding to the target scenes, wherein the target user information is determined based on the target user identifications;

and the brain map rendering unit is used for rendering a target brain map based on the target strategy path, and the target brain map is used for providing a path for processing the strategy required by the target user.

In a third aspect, there is provided an electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, which when executed by the processor performs the steps of the data processing method as in the first aspect.

In a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the data processing method as in the first aspect.

It can be seen that in the embodiment of the present application, a plurality of different scenes can be divided in advance, each scene corresponds to a policy brain graph capable of solving a user's appeal in the scene, when a target client feeds back an appeal to an agent, a target scene corresponding to the appeal can be determined from the plurality of different scenes, then a target policy path respectively matched with target user information and the target user's appeal is determined from the target policy brain graph corresponding to the target scene, and finally the target policy path is rendered to obtain a target brain graph. Because the target brain graph only comprises the target strategy path, when the agent determines the solution for the target user from the target brain graph, the solution meeting the requirements of the target user can be provided for the target user based on the strategy path in the target brain graph quickly, and the data processing efficiency is greatly improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

fig. 1 is a flow chart of a data processing method according to an embodiment of the present application.

FIG. 2 is an exemplary diagram of a policy brain graph in a resource management class appeal scenario in a data processing method according to one embodiment of the application.

Fig. 3 is a flowchart illustrating an application of the data processing method according to an embodiment of the present application in a practical scenario.

Fig. 4 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application.

Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. The reference numerals in the present application are only used for distinguishing the steps in the scheme, and are not used for limiting the execution sequence of the steps, and the specific execution sequence controls the description in the specification.

In order to cope with the problem that the existing brain map scheme for processing the user appeal can only show the complete brain map for combing all possible user appeal conditions, so that the efficiency of determining a brain map strategy path corresponding to the user appeal is low from the complete brain map when aiming at a specific user appeal.

The method comprises the steps that a plurality of different scenes can be divided in advance, each scene corresponds to a strategy brain graph capable of solving the user's appeal in the scene, when a target client feeds back the appeal to the seat, a target scene corresponding to the appeal can be determined from the plurality of different scenes, then a target strategy brain graph corresponding to the target scene is determined, target strategy paths respectively matched with target user information and the target user's appeal are determined, and finally the target strategy paths are rendered to obtain the target brain graph. Because the target brain graph only comprises the target strategy path, when the agent determines the solution for the target user from the target brain graph, the solution meeting the requirements of the target user can be provided for the target user based on the strategy path in the target brain graph quickly, and the data processing efficiency is greatly improved.

The embodiment of the application provides a data processing method which is applied to electronic equipment. As shown in fig. 1, the scheme provided by the embodiment of the application includes:

s110, acquiring target user appeal, wherein the target user appeal carries a target user identifier.

The electronic device may be, for example, a notebook computer, a desktop computer, an industrial computer, a background server, or the like.

Wherein the target user appeal is used to represent the appeal of the target user. The target user identifier is used for identifying the identifier of the target user, and the identifier can comprise an identity identifier (such as an identity card number and the like used for indicating the unique identifier of the identity) of the target user, an identifier of unique identifier user information such as a mobile phone number of the target user and the like.

It should be appreciated that the target user appeal may include, in addition to the target user identification, the point of complaint, appeal, and condition of the target user. The complaint points are used for indicating the problem category of the target user, the complaint is used for indicating the requirement of the target user, and the conditions are used for indicating the condition factors of the target user for solving the complaint. Taking the "personnel service class" user appeal as an example, the complaint points of such class of user appeal may include: complaints such as resource management and control personnel problems (attitude class, policy class., customer service personnel problems (attitude class, business operation class …), electric sales personnel problems (harassment class, marketing class …). The appeal of this category of user appeal may include "require sores", "stop marketing", and so forth. The conditions for such a category of user appeal may include a conditional factor of "whether the user himself/herself is asking", "whether there is a loan transacted", and the like.

Optionally, in order to improve data processing efficiency, the knowledge base documents for processing the user appeal may be classified in advance, and the policy paths corresponding to the different scenes are generated according to the user appeal corresponding to each different scene and the corresponding policy schemes in the knowledge base documents, and finally the policy paths of the different scenes are rendered to obtain the corresponding policy brain graph.

Specifically, before obtaining the target user appeal, the method further includes:

acquiring user complaint and strategy paths corresponding to a plurality of scenes;

generating a strategy brain map corresponding to the scenes based on the user complaints and strategy paths corresponding to the scenes; and each strategy brain graph corresponding to the scene comprises user complaints and strategy paths corresponding to the scene.

The policy brain map corresponding to the scene includes user appeal corresponding to the scene, which is all user appeal that the user may possibly propose in the scene. The policy brain map corresponding to the scene comprises a policy path corresponding to the scene, which is a path for providing a solution policy for all user requirements corresponding to the scene. It should be appreciated that the policy brain map corresponding to each scenario may be consolidated based on historical data, and may be stored for ease of agent review in a knowledge base document for handling user appeal, where user complaints presented by the user in each scenario and policy paths in that scenario are recorded.

Optionally, the plurality of scenes includes at least one of: a credit class appeal scene, an illegal class appeal scene, a resource management and control class appeal scene, a personnel service class appeal scene, a cost class appeal scene and a business rule class appeal scene.

The credit investigation type appeal scene is a scene in which bad records appear on the credit investigation of the user if the user overdue is not repayment, and the user hopes to modify the credit investigation records.

Illegal complaints include those in which the user receives illegal telephone calls or short message notifications, and the property security of the user is threatened, and hopefully evades such risks.

The resource management and control class appeal scene is a scene that a user is overdue and not repayment is not carried out, and the user is reminded of timely repayment.

The personnel service class appeal scene is a scene that if a user does not pay back due to overdue, the relevant staff reminds that the payment time is bad, and the user hopes to improve the service.

The fee-like appeal scene is such as a scene that the user has a credit card annual fee and does not pay, and the user hopes to exempt from the credit card annual fee.

The service rule class appeal scenario is a scenario in which a user has a question about a charging rule for some service charges (such as repayment interest of a credit card), and wishes to change the charging rule.

FIG. 2 is an exemplary diagram of a policy brain graph in a resource management class appeal scenario in a data processing method in accordance with one embodiment of the application. In fig. 2, the resource management class complaints in the resource management class complaint scenario may include a user principal complaint and a third party complaint, wherein the complaint points of the user principal complaint may include: (1) multiple calls, multiple short messages, contact units, resource management and control contacts, impersonation public inspection method, management and control of family, friends and relatives, and the like; (2) error solution and error operation; (3) the promise of feedback resource management and control personnel is not reached; (4) feedback of repayment is still resource-controlled. The complaints corresponding to the complaint point (1) may include: stopping reminding, reducing the fee rate, modifying credit record and reimbursement. The complaint points of the third party complaints can comprise (1) telephone nuisance, short message nuisance, contact units and impersonation resource management and control personnel; (2) error solution and error operation; (3) the promise of feedback resource management and control personnel is not reached; (4) feedback of repayment is still resource-controlled. The policy brain graph in the resource management class appeal scenario shown in FIG. 2 is only one example and should not be construed as limiting the specific policies of the policy brain graph in the present appeal scenario.

For example, in the phone-based agent service, when receiving an incoming call of a target user, in order to improve the efficiency of acquiring the complaint of the target user, when the target user selects "complaint advice", the target user may be guided by intelligent agent voice to select a scene meeting the requirements from among a credit-type complaint scene, an illegal-type complaint scene, a resource management-type complaint scene, a personnel service-type complaint scene and a fee-type complaint scene. After the target user selects a personnel service class appeal scene, the target user is guided by voice to select the appeal point of the target user from the complaint points of resource management personnel problems (attitude class, strategy class, etc.), customer service personnel problems (attitude class, business operation class …), electric sales personnel problems (harassment class, marketing class …) and the like, the appeal of the target user is selected from the appeal of 'requiring sorry', 'stopping marketing', and the like, and the target user appeal of the target user can be quickly obtained after the condition of the target user is selected from the condition factors of 'whether customer personals complaint', 'whether loan is transacted', and the like.

Or, in the phone-based agent service, when receiving the incoming call of the target user, the phone-based agent service can also communicate with the target user through the manual agent to acquire the target customer complaint scene of the target user, and the information related to the target customer complaint such as the complaint point, the complaint and the condition of the target user in the target scene.

S120, determining a target scene corresponding to the target user appeal from the plurality of scenes.

Optionally, the determining, from the multiple scenes, a target scene corresponding to the target user appeal includes:

responding to target customer complaint requirements of target users, and sending a target scene list associated with target user complaints to target terminal equipment of the target users so that terminals of the target users display the target scene list to the target users, wherein the target scene list comprises a plurality of scenes associated with the target user complaints;

and receiving a target scene from target terminal equipment, wherein the target scene is determined by the target terminal based on the selection operation of the target user on the target scenes in a target scene list.

The target scene list can be displayed to the target user in at least one of a voice broadcasting form, a text list form and a video playing form.

It should be appreciated that the order of magnitude of the categories of the relevant scenes of the user appeal may be larger in the scene perfecting process along with the user appeal, and in order to improve the efficiency of processing the user appeal, when the target scene list is displayed for the target user, the multiple scenes may be pre-screened based on the target user appeal, and the target scene list associated with the target user appeal may be screened from the multiple scenes. The pre-screening method can include a method of matching keywords in the target user's appeal with keywords in a plurality of scenes, a method of matching basic information of the target user with the scenes, and the like.

Optionally, as an example, in the phone-based agent service, when receiving an incoming call of a target user, in order to improve efficiency of determining a target scenario corresponding to a target user's appeal, when the target user selects "complaint advice", the target user may be guided by intelligent agent voice to select a target scenario conforming to its appeal from a credit class appeal scenario, an illegal class appeal scenario, a resource management class appeal scenario, a personnel service class appeal scenario, and a fee class appeal scenario. For example, the target user can be guided by voice to select a credit class appeal scene by 1, an illegal class appeal scene by 2, a resource management class appeal scene by 3, a personnel service class appeal scene by 4, and a fee class appeal scene by 5". When the target user selects the target scene, for example, the target user presses 1, the target scene can be determined to be a credit investigation type resort scene directly in response to the selection of the target user.

Or in the seat service based on telephone, when receiving the incoming of the target user, when the target user selects the complaint proposal, the target user appeal of the target user voice description can be obtained, and then the target user appeal of the voice description is analyzed to determine the target scene corresponding to the target appeal requirement from the credit appeal scene, the illegal appeal scene, the resource management and control appeal scene, the personnel service appeal scene and the expense appeal scene.

Optionally, as an example, in a phone-based agent service, agents with different permissions may view and provide different policy paths to the policy brain map in multiple scenarios of the user. For example, the first-level agent can view and provide a strategy brain map of a plurality of scenes which can provide a preliminary solution for the user, for example, a solution for stopping marketing can be provided for harassment calls or short message marketing existing in personnel service appeal scenes; the second-level agent can view and provide a policy brain graph of a further solution for the user in a plurality of scenes of the client, for example, the policy brain graph can provide a solution for avoiding fees for the user with higher loan rate in the resource management and control type resort scene.

As another example, in a dialog-based agent service, upon receiving a target user selection of "complaint advice," a category list of scenes of user complaints may be presented, including a credit-class complaint scene, an illegal-class complaint scene, a resource-management-class complaint scene, a personnel-service-class complaint scene, and a fee-class complaint scene, and the target user may directly select a target scene that meets his user's complaint in the category list of user complaints. When the target user selects a target scene, for example, the target user clicks the credit complaint scene, the target scene can be determined to be the credit complaint scene directly in response to the selection of the target user.

Or in the seat service based on the dialog box, when receiving a target user selection 'complaint advice', the dialog box of the target user text description can be provided, the target user appeal of the target user text description is obtained, and then the target user appeal of the text description is analyzed, so that the target scene corresponding to the target user appeal is determined from the credit-type appeal scene, the illegal-type appeal scene, the resource management-control-type appeal scene, the personnel service-type appeal scene and the expense-type appeal scene.

S130, determining target strategy paths which are respectively matched with target user information and target user appeal from target strategy brain diagrams corresponding to target scenes, wherein the target user information is determined based on target user identification.

The target user information may include basic information and accounting information of the target user, such as name, age, occupation, etc. of the target user, and accounting information of the target user, such as loan status, loan amount, loan term, etc.

Optionally, in order to improve the efficiency and accuracy of determining the target policy path, the target policy brain graph corresponding to the target scene may include a plurality of nodes, and each node may record corresponding metadata for matching with customer information and customer complaint requirements. Specifically, determining, from a target policy brain graph corresponding to a target scene, a target policy path that is respectively matched with target user information and target user appeal, includes:

Matching the target user information and the target user appeal with metadata of a plurality of nodes in the target strategy brain graph respectively to obtain matching degrees of the target user information and the target user appeal and the metadata of the plurality of nodes in the target strategy brain graph;

determining a target node matched with the target user client information and the target user appeal from a plurality of nodes in the target strategy brain graph based on the matching degree;

a target policy path is determined based on the target nodes and the association between the target nodes.

The metadata of the node in the policy brain graph may include a description of the node. As an example, metadata of a node in a policy brain graph may indicate whether the node is a complaint point, a complaint, or a specific policy scheme, and a superior node and a subordinate node that have an association relationship with the node.

As one example, assuming that the target user is indicated in the target user information that there is an overdue payment record and the target user appeal indicates that he wants to eliminate the overdue payment record, one or more target nodes that match the overdue payment record and that are due to personal overdue may be determined from a plurality of nodes in the target policy brain graph based on the target user information and the target user appeal. The complaint points of the one or more target nodes may include overdue repayment records, with the complaint being to eliminate overdue repayment records. And determining a target policy path matched with the target user information and the target user appeal based on the one or more target nodes and the upper and lower level relevance between the one or more target nodes.

And S140, rendering a target brain graph based on the target strategy path, wherein the target brain graph is used for providing a strategy path for processing target user requirements.

The target brain map is rendered based on the target strategy path, and specifically, the target strategy path can be rendered through a brain map engine to obtain the target brain map.

Optionally, after the target brain map is rendered based on the target policy path, the target policy path provided by the target brain map may have a situation that the customer complaint requirement of the target customer cannot be immediately solved, for example, the policy path provided by the target brain map needs to be further audited, and after the audit is passed, the complaint mentioned in the customer complaint requirement of the target customer can be solved. In this case, a complaint work sheet may be created for the target user's complaint in order to track the resolution of the target user's complaint. Specifically, the method provided in the embodiment of the present specification further includes:

and establishing a target user work order based on the source information of the target user appeal, the target user information and the target brain map.

Optionally, the target user worksheet may be established on the premise that the target user's requirements presented by the target user cannot be resolved in text or voice communication between the agent and the target user. When it is determined based on the target user's appeal that the user's appeal cannot be resolved in this time text or voice communication with the target user, a customer complaint work order for the target customer may be established.

Optionally, in order to timely validity of each policy path in the policy brain graph, embodiments of the present disclosure may bury a policy path that provides a user with a need to handle his complaint, so as to obtain whether a solution provided in the policy path is adopted by the user. Specifically, after the target user worksheet is established, the method provided in the embodiment of the present disclosure further includes:

and burying points in the strategy paths in the target brain graph in the target user work order, wherein the burying points are used for acquiring feedback opinions of the target user aiming at the target brain graph.

Optionally, in order to summarize the validity of each policy path in the policy brain graph, determine the utilization rate of each policy path so as to optimize the corresponding policy brain graph in each scene, in this embodiment of the present disclosure, after a preset period of time, for example, after every other week or every other month, feedback comments of the policy brain graph corresponding to multiple scenes, for example, the adoption rate, the rejection rate, and the like, of each policy path in the policy brain graph may be obtained. Specifically, after burying points in the policy paths in the target brain graph in the target user work order, the method provided by the embodiment of the specification further includes:

Acquiring feedback opinions of strategy brain graphs corresponding to a plurality of scenes;

and optimizing the strategy brain graphs corresponding to the multiple scenes based on the feedback opinion of the strategy brain graphs corresponding to the multiple scenes.

For example, the priority of the policy paths with higher user adoption rate can be adjusted upwards, the priority of the policy paths with lower user adoption rate can be adjusted downwards, and even some policy paths with lower user adoption rate than a set threshold value can be deleted. Or, some policy paths can be added according to the demands of the users or the communication results with the users.

Fig. 3 is a flowchart illustrating an example of a data processing method applied to a practical scenario, where a phone-based agent service is taken as an example, according to an embodiment of the present application, including:

s31, acquiring the mobile phone number of the target user, and acquiring the basic information and the financial information of the target user based on the mobile phone number of the target user.

Specifically, after the target user makes a call with the seat, the mobile phone number of the target user can be obtained, and basic information such as the name, the age, the occupation and the like of the target user and financial information such as the borrowing and lending situation and the like can be obtained based on the mobile phone number of the target user.

S32, acquiring a target scene corresponding to target user appeal of the target user.

Specifically, after the target user is connected with the agent, the target user can be guided to select a target scene, or the user appeal of the target user voice description is obtained, and the user appeal is analyzed to obtain the target scene corresponding to the target user appeal.

S33, determining JSON data of a target strategy brain graph corresponding to the target scene.

The target strategy brain graph comprises strategy paths for processing all possible customer complaint demands in a target scene.

S34, filtering and obtaining JSON data of a target strategy path associated with the target user from a target strategy brain graph based on basic information and financial information of the target user and target user requirements of the target user.

S35, rendering the JSON data of the target strategy path into a target brain graph based on a brain graph rendering tool.

S36, obtaining feedback results of the target user on the strategy paths in the target brain graph, wherein the feedback results comprise agreements, disagreements and considerations.

And recording a feedback result of the target user on the strategy paths in the target brain chart into a strategy list of the corresponding strategy paths in the target brain chart, so as to count the use rate or the adoption rate of each strategy path.

S37, determining whether a policy path in the target brain diagram solves the customer complaint requirement of the target user.

If the strategy path in the target brain diagram is determined to solve the customer complaint requirement of the target user, ending the flow. If it is determined that the policy path in the target brain graph does not address the customer complaint needs of the target user, S38 is performed.

S38, creating a target user work order based on the customer complaint requirements of the target user.

S39, submitting the target user work order to submit the target user work order to the seat with higher authority for solving.

By adopting the method provided by the embodiment of the application, a plurality of different scenes can be divided in advance, each scene corresponds to a strategy brain map capable of solving the user's appeal in the scene, when a target client feeds back the appeal to the seat, the target scene corresponding to the appeal can be determined from the plurality of different scenes, then the target strategy brain map corresponding to the target scene is determined, and finally the target strategy path is rendered to obtain the target brain map. Because the target brain graph only comprises the target strategy path, when the agent determines the solution for the target user from the target brain graph, the solution meeting the requirements of the target user can be provided for the target user based on the strategy path in the target brain graph quickly, and the data processing efficiency is greatly improved.

In order to solve the problems in the prior art, as shown in fig. 4, an embodiment of the present application further provides a data processing apparatus 400, including:

a requirement acquisition unit 401, configured to acquire a target user requirement, where the target user requirement carries a target user identifier;

a scene determination unit 402, configured to determine a target scene corresponding to the target user appeal from a plurality of scenes;

a path determining unit 403, configured to determine, from a target policy brain graph corresponding to the target scene, target policy paths that are respectively matched with target user information and the target user appeal, where the target user information is determined based on the target user identifier;

and the brain map rendering unit 404 is configured to render a target brain map based on the target policy path, where the target brain map is used to provide a path for processing the target user's policy.

Optionally, in one embodiment, before the demand acquisition unit 401 acquires the target user's demand, the apparatus further includes:

the path acquisition unit is used for acquiring user complaint and strategy paths corresponding to the scenes;

the brain map generating unit is used for generating a strategy brain map corresponding to the scenes based on the user complaints and strategy paths corresponding to the scenes; and each strategy brain graph corresponding to the scene comprises user complaints and strategy paths corresponding to the scene.

Optionally, in an embodiment, the path determining unit 403 is configured to:

matching the target user information and the target user appeal with metadata of a plurality of nodes in the target strategy brain graph respectively to obtain matching degrees of the target user information and the target user appeal with metadata of a plurality of nodes in the target strategy brain graph;

the target policy path is determined based on the target node and the association between the target nodes.

Optionally, in an embodiment, after the brain map rendering unit 404 renders the target brain map based on the target policy path, the apparatus further includes:

and the work order establishing unit is used for establishing a target user work order based on the source information of the target user appeal, the target user information and the target brain graph.

Optionally, in one embodiment, the apparatus further comprises:

the embedded point unit is used for embedding points into the strategy paths in the target brain graph in the target user work order, and the embedded points are used for acquiring feedback opinions of the target user on the target brain graph; .

Optionally, in one embodiment, the apparatus further comprises:

the feedback unit is used for acquiring feedback comments of the strategy brain graphs corresponding to the scenes;

and the optimizing unit is used for optimizing the strategy brain graphs corresponding to the scenes based on the feedback opinion of the strategy brain graphs corresponding to the scenes.

Optionally, in one embodiment, the plurality of scenes includes at least one of: a credit class appeal scene, an illegal class appeal scene, a resource management and control class appeal scene, a personnel service class appeal scene, a cost class appeal scene and a business rule class appeal scene.

The data processing apparatus 400 can implement the method of the method embodiment of fig. 1 to 3, and specifically, the data processing method of the embodiment shown in fig. 1 to 3 may be referred to, which is not described herein.

Fig. 5 is a schematic structural view of an electronic device according to an embodiment of the present specification. Referring to fig. 5, at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.

The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 5, but not only one bus or type of bus.

And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.

The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs, forming the data processing device on a logic level. The processor is used for executing the programs stored in the memory and is specifically used for executing the following operations:

According to the electronic equipment provided by the embodiment of the specification, a plurality of different scenes can be divided in advance, each scene corresponds to a strategy brain graph capable of solving the user appeal in the scene, when a target client feeds back the appeal to the seat, a target scene corresponding to the appeal can be determined from the plurality of different scenes, then a target strategy path respectively matched with target user information and the target user appeal is determined from the target strategy brain graph corresponding to the target scene, and finally the target strategy path is rendered to obtain the target brain graph. Because the target brain graph only comprises the target strategy path, when the agent determines the solution for the target user from the target brain graph, the solution meeting the requirements of the target user can be provided for the target user based on the strategy path in the target brain graph quickly, and the data processing efficiency is greatly improved.

The method performed by the data processing apparatus disclosed in the embodiments shown in fig. 1 to 3 of the present specification may be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of this specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present specification may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.

The electronic device may also execute the methods of fig. 1 to 3 and implement the functions of the embodiments of the data processing apparatus shown in fig. 1 to 3, which are not described herein again.

The present description also proposes a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, enable the portable electronic device to perform the method of the embodiments shown in fig. 1-3, and in particular to perform the operations of:

The computer readable storage medium provided in the embodiments of the present disclosure may divide a plurality of different scenes in advance, each scene corresponds to a policy brain graph capable of solving a user's appeal in the scene, when a target client feeds back an appeal to an agent, a target scene corresponding to the appeal may be determined from the plurality of different scenes, then a target policy brain graph corresponding to the target scene may be determined, and finally the target policy path may be rendered to obtain a target brain graph. Because the target brain graph only comprises the target strategy path, when the agent determines the solution for the target user from the target brain graph, the solution meeting the requirements of the target user can be provided for the target user based on the strategy path in the target brain graph quickly, and the data processing efficiency is greatly improved.

Of course, in addition to the software implementation, the electronic device in this specification does not exclude other implementations, such as a logic device or a combination of software and hardware, that is, the execution subject of the following process is not limited to each logic unit, but may also be hardware or a logic device.

The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

In summary, the foregoing description is only a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the protection scope of the present specification.

The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.

Claims

1. A method of data processing, the method comprising:

2. The method of claim 1, wherein prior to the obtaining the target user appeal, the method further comprises:

Acquiring user complaint and strategy paths corresponding to the scenes;

3. The method of claim 1, wherein determining target policy paths that match target user information and the target user appeal, respectively, from the target policy brain map corresponding to the target scene comprises:

4. The method of claim 1, wherein after the rendering of the target brain map based on the target policy path, the method further comprises:

5. The method of claim 4, wherein the method further comprises:

6. The method of claim 5, wherein the method further comprises:

acquiring feedback comments of strategy brain graphs corresponding to the scenes;

and optimizing the strategy brain graphs corresponding to the scenes based on the feedback opinion of the strategy brain graphs corresponding to the scenes.

7. The method of any of claims 1-6, wherein the plurality of scenarios includes at least one of: a credit class appeal scene, an illegal class appeal scene, a resource management and control class appeal scene, a personnel service class appeal scene, a cost class appeal scene and a business rule class appeal scene.

8. A data processing apparatus, the apparatus comprising:

9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor performs the steps of the data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the data processing method according to any of claims 1 to 7.