CN112766935A - Method, device, computing equipment and medium for processing bill service - Google Patents

Abstract

The present disclosure provides a method for processing a ticket service, the method comprising: responding to the received trigger signal of the bill service, and circularly executing state conversion operation until the current state of the service is converted into an end state; wherein the state transition operation comprises: acquiring the current state and the action to be executed of the service; determining a next state corresponding to the current state and the action to be executed by inquiring the state transition table; executing the action to be executed; and setting the next state as the new current state. The present disclosure also provides an apparatus for processing ticket business, a computing device, a computer storage medium and a computer program product. The method, apparatus, computing device, medium, and program product for processing ticket transactions may be used in the financial or other fields.

Description

Method, device, computing equipment and medium for processing bill service

Technical Field

The present disclosure relates to the field of computer systems, and more particularly, to a method, apparatus, a computing device, a computer storage medium, and a computer program product for processing ticket traffic.

Background

In the related technology, when a bill is processed, the processing states and processing flows of the bill, such as issuance, storage, rechecking, cashing, cancellation, deletion, long-time no-person claim, loss report and the like, of the bill are discrete, manual triggering is needed, each state needs to be manually identified and the state is circulated, so that errors are easy to occur, and the labor cost is high.

Disclosure of Invention

One aspect of the present disclosure provides a method of processing a ticket service, including: responding to the received trigger signal of the bill service, and circularly executing state conversion operation until the current state of the service is converted into an end state; wherein the state transition operation comprises: acquiring the current state and the action to be executed of the service; determining a next state corresponding to the current state and the action to be executed by inquiring a state transition table; executing the action to be executed; and setting the next state as a new current state.

Optionally, the method further comprises: recording state transition path information corresponding to the state transition operation each time a state transition operation is performed, wherein the state transition path information at least includes the following element information: a state before state transition, a state after state transition, and an action for triggering state transition; determining behavior rule data according to the recorded state conversion path information; and configuring personalized configuration data of the user according to the behavior rule data.

Optionally, the determining behavior rule data according to the recorded state transition path information includes: determining hotspot path information in the recorded state conversion path information, and storing the hotspot path information to a hotspot library; acquiring additional service information related to the hotspot path information aiming at each hotspot path information in the hotspot library; and determining behavior rule data according to the hotspot path information and the additional service information.

Optionally, the determining hotspot path information in the recorded state transition path information includes: determining the number of each type of state transition path information in the recorded state transition path information; for each state transition path information, determining a weight of the state transition path information according to the number of the state transition path information; and determining the state transition path information as hotspot path information under the condition that the weight of the state transition path information is greater than a preset weight threshold.

Optionally, the storing the hotspot path information to a hotspot library includes: under the condition that the quantity of the hotspot path information recorded in the hotspot library does not reach the upper limit of the preset quantity, recording the hotspot path information to the hotspot library; and recording the hotspot path information into a hotspot library under the condition that the number of the hotspot path information recorded in the hotspot library reaches a preset number upper limit, and deleting the hotspot path information with the lowest weight value in the hotspot library.

Optionally, the additional service information includes at least one of the following factor information: amount, user name, account number, date and currency; the determining behavior rule data according to the hotspot path information and the additional service information includes: and determining element information with an incidence relation in the hotspot path information and the additional service information as the behavior rule data.

Optionally, the configuring, according to the behavior rule data, personalized configuration data of the user includes: acquiring behavior data of a user; determining target behavior rule data with a similarity parameter larger than a similarity threshold value between the target behavior rule data and the behavior data of the user from the determined behavior rule data; and configuring personalized configuration data of the user according to the target behavior rule data.

Optionally, the personalized configuration data includes prompt information and prompt time, and the method further includes: and displaying the prompt information to the user at the prompt time.

Another aspect of the present disclosure provides an apparatus for processing a ticket service, including: the receiving module is used for receiving a trigger signal of the bill service; the state conversion module is used for circularly executing the state conversion operation under the condition of receiving the trigger signal of the bill service until the current state of the service is converted into the end state; wherein the state transition module comprises: the obtaining submodule is used for obtaining the current state and the action to be executed of the service; the state determination submodule is used for determining the next state corresponding to the current state and the action to be executed by inquiring a state conversion table; the execution submodule is used for executing the action to be executed; and the state setting submodule is used for setting the next state as a new current state.

Another aspect of the disclosure provides a computing device comprising: one or more processors; storage means 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 implement the method as described above.

Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.

According to yet another aspect of an embodiment of the present disclosure, there is provided a computer program product comprising computer programs/instructions which, when executed by a processor, implement a method according to an embodiment of the present disclosure.

According to the embodiment of the disclosure, the whole construction of the service flow engine is completed through technologies such as a state conversion table and an automaton in the whole construction process of the service flow engine, so that the service processing process is automated, and the error probability and the labor cost are reduced. In addition, the datamation of the service processing process is realized, and data support is provided for subsequent data processing.

Drawings

For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:

fig. 1 schematically shows a bill processing center cluster architecture diagram to which the method of processing a bill service and the apparatus for processing a bill service can be applied according to an embodiment of the present disclosure;

FIG. 2 schematically shows a flow diagram of a method of processing a ticket service according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow diagram of a method of processing a ticket service according to another embodiment of the present disclosure;

FIG. 4 schematically illustrates a business process flow diagram of the field of ticket processing, in accordance with an embodiment of the present disclosure;

FIG. 5 schematically shows a block diagram of an apparatus for processing ticket traffic according to an embodiment of the present disclosure; and

FIG. 6 schematically illustrates a block diagram of a computer system suitable for implementing the methods described in embodiments of the present disclosure, in accordance with embodiments of the present 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 illustrative only 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 disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not 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 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 is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have 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.).

Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.

The embodiment of the disclosure provides a method for processing bill service and a device for processing bill service capable of applying the method. Responding to a received trigger signal of a bill service, and circularly executing state conversion operation until the current state of the service is converted into an end state; wherein the state transition operation comprises: acquiring the current state and the action to be executed of the service; determining a next state corresponding to the current state and the action to be executed by inquiring the state transition table; executing the action to be executed; and setting the next state as the new current state.

It should be noted that the method and apparatus for processing a ticket service according to the embodiments of the present disclosure may be used in the financial field, and may also be used in any field other than the financial field.

According to the embodiment of the disclosure, the same-city bill exchange refers to the cross-row bill exchange in the same city or the same region. Exchanging service with city: one of the two parties of the collection and payment is an account opened by a certain bank outlet, the other party is an account of a cross-system financial institution, and the mutual fund exchange business between the two parties is the same city exchange business. The same city exchange service can be processed by adopting a mode of foreground teller input and background teller recheck.

Next, an example of "propose a substitute payment in the same city" will be described with the payee as the first bank and the payer as the second bank. A person A gives a bill of a first bank to a second bank client B, and the second bank client B holds the bill of the first bank to be converted into the second bank and deposits the sum of the bill into a second bank account. The second bank pays the first bank and then makes settlement with the first bank.

The following describes "withdrawal and payment in the city" with the payee as the first bank and the payer as the second bank. The second bank client B sends the bill of the second bank to a person A, the person A holds the bill of the second bank to exchange the bill with the first bank, and deposits the amount into the first bank account, namely the first bank pays the second bank, and then the first bank and the second bank settle money.

The user behavior refers to the user purpose and the social activity of the user, etc., pointed by the user business data in the bill processing center system, and the sum of all the social behaviors which can be digitalized and are expanded to the outside of the bill processing center.

Fig. 1 schematically shows a bill processing center cluster architecture diagram to which the method of processing a bill service and the apparatus for processing a bill service can be applied according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, the bill processing center cluster according to the embodiment may include a channel-side device 10, anonline processing center 20, and a bank-side device 30.

According to the embodiment of the present disclosure, the channel-side device 10 may refer to, for example, a server or a client device disposed on the channel side, and may be used for depositing a check by a check machine, depositing a check by internet banking, depositing a check by a mobile phone, depositing a check by a counter, or the like.

Online processing center 20 may refer to an unmanned online ticket processing center that includes adata flow engine 21. Theonline processing center 20 may be configured to access the bill data stored by the channel side device 10, perform operations such as image archiving, monitoring, and information registration on the bill data, and then complete identification of the data flow through thedata flow engine 21, and complete collection of the data, so as to facilitate operations such as subsequent data cleaning, collection, and behavior discovery. Theonline processing center 20 may also include amonitoring information repository 22, anexchange information repository 23, and aticket imaging repository 24. Themonitoring information base 22 can be used for storing monitoring information, theexchange information base 23 can be used for storing exchange information of bills, and thebill image base 24 can be used for storing images of bills.

The bank-side device 30 may refer to a server or a client device provided on the bank side. The bank-side device 30 may be configured to access theonline processing center 20, acquire the ticket data processed by theonline processing center 20, and perform information checking, accounting processing, information registration, and the like on the ticket data. Thebank side device 30 includes abusiness flow engine 31, and intelligent automatic processing of business processes can be completed through the business flow engine. The bank-side devices 30 may be N, where N is a positive integer greater than 0.

The on-line processing center 20 may further include a behavior discovery machine 25, which may be configured to analyze data generated by thedata flow engine 21 and theservice flow engine 31 to discover user behavior.

Fig. 2 schematically shows a flow chart of a method of processing a ticket service according to an embodiment of the present disclosure.

As shown in fig. 2, the method includes operations S210 to S260.

In operation S210, a trigger signal of a ticket service is received.

According to the embodiment of the disclosure, a user can input bill data through the channel side equipment and send a trigger signal of bill service to the bank side equipment. And the bank side equipment responds to the received trigger signal of the bill service and circularly executes the state conversion operation until the current state of the service is converted into the end state.

In operation S220, it is determined whether the current state of the service is converted into an end state. If the current state is not converted into the end state, the state conversion operation is performed, i.e., operations S230 to S260. And if the current state is converted into the end state, ending the state conversion operation and continuously waiting for the next trigger of the bill service.

In operation S230, a current state of the service and an action to be performed are acquired.

In operation S240, a next state corresponding to the current state and the action to be performed is determined by referring to the state transition table.

According to the embodiment of the present disclosure, states and actions involved in the service execution process, and correspondence between the states and actions are preconfigured in the state transition table. The state is updated after each execution of an action. Illustratively, the state transition table includes fields for original state, action, post state, etc. The action refers to a business operation executed by the system, the original state refers to a state before the action is executed, and the later state refers to a state after the action is executed under the condition that the current state is in the original state.

Based on this, the current state and the action to be executed are used as indexes, a data item matched with the current state and the action to be executed is searched in the state transition table, and the later state in the data item is read as the next state.

In operation S250, an action to be performed is performed.

In operation S260, the next state is set as a new current state. And then jumps to execute operation S220.

When the related technology is used for processing bill services, the processing states and processing flows of bills, such as issuance, storage, rechecking, cashing, cancellation, deletion, long-time no-person claim, loss report and the like, of the bills are discrete, manual triggering is needed, each state needs to be manually identified and the states are circulated, so that errors are easy to occur, and the labor cost is high. According to the embodiment of the disclosure, the whole construction of the service flow engine is completed through technologies such as a state conversion table and an automaton in the whole construction process of the service flow engine, so that the service processing process is automated, and the error probability and the labor cost are reduced. In addition, the datamation of the service processing process is realized, and data support is provided for subsequent data processing.

Fig. 3 schematically shows a flow chart of a method of processing a ticket service according to another embodiment of the present disclosure.

As shown in fig. 3, the method includes operations S310 to S350.

In operation S310, a trigger signal of a ticket service is received.

In operation S320, it is determined whether the current state of the service is converted into an end state. If the current state is not converted into the end state, operation S330 is performed. And if the current state is converted into the end state, ending the state conversion operation and continuously waiting for the next trigger of the bill service.

In operation S330, a state transition operation is performed.

According to an embodiment of the present disclosure, the state transition operation may refer to the operations S230 to S260, which are not described herein again.

In operation S340, state transition path information corresponding to the state transition operation is recorded.

According to an embodiment of the present disclosure, state transition path information corresponding to a state transition operation is recorded every time the state transition operation is performed. Wherein, the state transition path information at least comprises the following element information: a state before state transition, a state after state transition, and an action to trigger state transition.

In operation S350, behavior rule data is determined according to the recorded state transition path information.

Operation S350 may include, for example, operations S351-S353 according to an embodiment of the present disclosure.

In operation S351, hotspot path information in the recorded state transition path information is determined, and the hotspot path information is stored in a hotspot library.

According to other embodiments of the present disclosure, a preset number upper limit may be set for the hotspot library. And recording the hotspot path information to the hotspot library under the condition that the quantity of the hotspot path information recorded in the hotspot library does not reach the preset quantity upper limit. And under the condition that the quantity of the hot spot path information recorded in the hot spot library reaches a preset quantity upper limit, recording the hot spot path information into the hot spot library, and deleting the hot spot path information with the lowest weight value in the hot spot library.

By setting the upper limit of the preset number of the hotspot databases, hotspot data can be screened for subsequent processing, the data volume of the subsequent processing is reduced, and the efficiency is improved.

In operation S352, for each piece of hotspot path information in the hotspot library, additional service information related to the hotspot path information is acquired.

According to an embodiment of the present disclosure, the additional service information may include, for example, at least one of the following factor information: amount, username, account, date, and currency.

In operation S353, behavior rule data is determined according to the hotspot path information and the additional service information.

According to the embodiment of the disclosure, element information having an association relationship in hotspot path information and additional service information can be determined as behavior rule data.

According to the embodiment of the disclosure, a deep learning model can be trained in advance, and the hot spot path information and the additional service information are determined to have the incidence relation element information by using the deep learning model. It should be noted that, in the process of practical application, other methods may be used to determine the association relationship between the element information, and this disclosure is not limited in this regard.

In operation S360, personalized configuration data of the user is configured according to the behavior rule data.

According to an embodiment of the present disclosure, operation S360 may include, for example, acquiring behavior data of a user, determining target behavior rule data, from the determined behavior rule data, of which a similarity parameter with the behavior data of the user is greater than a similarity threshold, and then configuring personalized configuration data of the user according to the target behavior rule data. The similarity parameter can be determined according to the number of the same or similar key elements contained in the behavior rule data and the behavior data of the user. Illustratively, in this embodiment, the similarity parameter is equal to the number of the behavior rule data and the behavior data of the user, which contain the same or similar key elements, and the similarity threshold is equal to the number of the key elements contained in the behavior rule data.

According to another embodiment of the present disclosure, the similarity parameter between the behavior law data and the behavior data of the user may be determined by a pre-trained deep learning model.

According to embodiments of the present disclosure, the personalized configuration data may include, for example, a reminder information and a reminder time. The method may further comprise: and displaying prompt information to the user at the prompt time according to the configured personalized configuration data.

According to another embodiment of the present disclosure, the personalized configuration data includes default information of the ticket, and the method may further include: and then when the user applies to input the bill information, displaying a bill input form filled with the default information to the user so as to facilitate the filling of the user.

According to a further embodiment of the present disclosure, the personalized configuration data may further include feature information of the user, and the method further includes: and recommending the information to the user according to the characteristic information of the user.

In this embodiment, the service flow engine can be used to complete the intelligent automatic processing of the service flow, so that the bill service processing flow is intelligent and automatic. The construction of the business flow engine can be completed by modeling the business flow field, classifying and collecting, intelligently storing, automatically triggering (for example, similar scene bill intelligent flow triggering) and the like, so as to realize the automation of the business process on the business process. In addition, the business process can be digitalized by performing domain modeling on the business process, namely establishing a model according to a specific scene used by the business.

Referring to FIG. 4, the field modeling method is further illustrated by taking the field of bill processing as an example. Those skilled in the art will appreciate that the following example embodiments are only for the understanding of the present disclosure, and the present disclosure is not limited thereto.

Fig. 4 schematically shows a business process flow diagram of the field of ticket processing according to an embodiment of the present disclosure.

As shown in fig. 4, in the process of processing the ticket business, the actions performed by the system may include, for example, issuance, online banking issuance, subsidizing, updating, cashing, reimbursement, modification, loss reporting, refund, finalization, and the like, and the corresponding states may include, for example, initial state, not issued, not yet cashed, lost, refund, no-one acceptance, cancelled, deleted, redeemed, finalized, and the like.

Thus, in modeling for a business process domain, an action abstraction can be divided by domain into: { A issuance, B network silver issuance, C subsidy, D renewal, E redemption, F reimbursement, G modification, H loss report, I refund, J completion } and the like. The state abstraction can be divided into: {0 initial state, 1-not issued, 2-not redeemed, 3-redeemed, 4-reported lost, 5-refunded, 6-no-claim, 7-cancelled, 8-deleted, 9-corrected, 10-ended } etc.

And completing the data modeling of the state and the action through the process modeling, and completing the construction of a service flow engine state conversion table by combining the corresponding modeling. Illustratively, in the present embodiment, the traffic flow engine state transition table is shown in table 1, for example.

TABLE 1

After the state modeling is completed, the automatic business processing engine can be constructed based on the model.

It should be noted that the above-described flow of the ticket status is only an exemplary example in a business scenario, and in the process of practical application, the ticket processing flow may also include other flows, or may also be applied to other business scenarios, and this disclosure is not limited in this respect.

As shown in fig. 4, polling waits to receive a trigger for a ticket deposit action. When a signal triggering a bill deposit action is received, circularly executing the following operations by using a business flow engine: the method comprises the steps of obtaining a current state and action, determining a next action and state corresponding to the current state and action according to a state transition table, executing the next action, updating the current state to enable the next state to be a current state, and adding 1 to a weight value corresponding to the state transition process, namely the weight value of the state transition process is equal to the number of the state transition process, wherein the state transition process can be represented by paths between the states in fig. 4, and therefore the state transition process can also be called a state transition path.

And then, sequencing the state conversion paths according to the weight value, and selecting the state conversion paths with the weight values larger than a threshold value as hotspot path information. And recording the hotspot path information to a hotspot library.

In this embodiment, an upper limit of the number of hot spot path information in the hot spot library may be set. And under the condition that the quantity of the hot spot path information recorded in the hot spot library does not reach the upper limit of the quantity, when new hot spot path information is generated, recording the new hot spot path information to the hot spot library. Under the condition that the quantity of the hot spot path information recorded in the hot spot library reaches the upper limit of the quantity, when new hot spot path information is generated, the new hot spot path information is recorded into the hot spot library, and the hot spot path information with the lowest weight in the hot spot library is deleted.

For example, in this embodiment, the service flow engine core processing logic and the hotspot path information collection policy may be expressed as the following algorithm, for example.

Through the algorithm, automatic state circulation and automatic replacement of the hotspot library are completed. During service processing, the hot spot library can be searched preferentially to accelerate the searching speed. Meanwhile, the algorithm can also be incorporated into an algorithm library to support the automatic updating of the algorithm and other functions.

According to the embodiment of the disclosure, the whole construction of the service flow engine is completed through technologies such as a state conversion table, an automaton and hot spot replacement in the whole construction process of the service flow engine, so that the service processing process is automated, and the error probability and the labor cost are reduced. In addition, the datamation of the service processing process is realized, and data support is provided for subsequent data processing.

The method for constructing the data stream engine is further described with reference to the specific embodiments. Those skilled in the art will appreciate that the following example embodiments are only for the understanding of the present disclosure, and the present disclosure is not limited thereto.

According to the embodiment of the disclosure, the data flow engine is constructed to achieve the identification of the data flow and the collection of the data, so as to store and divide the data in a targeted manner, and convert the data into modes which are convenient to operate and process, such as subsequent data cleaning, acquisition and behavior discovery.

In order to achieve the aim more conveniently and quickly, the data storage part of the data stream engine is constructed based on the idea of model data stream storage, so that the data stream engine is closer to the real life when the data stream operation is carried out on the engine, and a real transaction scene can be better simulated.

According to the embodiment of the present disclosure, the implementation process of "model data stream storage" may include operations such as business scenario warehousing, business domain analysis, and business domain modeling. .

The service scenario library may include, for example: analyzing the service scene to be put in storage, and searching according to the established model to determine whether an object or a behavior similar to an object (for example, a state of the service when the service is executed) or a behavior (for example, an operation when the service is executed) in the service scene exists in the established model. And if similar objects or behaviors are found in the model, classifying the objects or behaviors in the service scene and the model into one class, and performing warehousing operation on the corresponding objects or behaviors. And if no similar object or behavior is found, performing two steps of business field analysis operation and business field modeling operation to establish a new business field model.

The business domain analysis may include, for example: and finding out the service scenes mapped with the service fields in real life through the service fields, carrying out detailed analysis on each service scene, and combing all the service scenes corresponding to the service fields.

The business domain modeling may include, for example: and completing the datamation of the service scene through the data model. More specifically, objects and behaviors in the business scene are identified, and the identified objects and behaviors are abstracted to generate a corresponding data model. Then, a corresponding business model library, an object or behavior model library and the like related data model library are established for storing the data models.

In conclusion, when the data stream engine is constructed, the model base construction of the whole business field is completed through the three operations. For new bills added subsequently, all objects and behaviors related in the whole life cycle of the bills are identified and classified into corresponding object or behavior field model libraries, so that the behaviors or the objects are discovered automatically, and the model libraries can be expanded by utilizing the model self-discovery technology.

More specifically, object element matching or object behavior matching can be performed through an established object model library or behavior model library, comparison of model element points is completed through an infinite approximation algorithm, and point positions of matched elements and matching approximation degrees are set through parameter thresholds. If the approach degree of the object or the behavior to the model is larger than the approach degree threshold value, the object or the behavior is classified into the model. And if the model approaching to the object or the behavior does not exist in the model library, automatically executing element analysis aiming at the object or the behavior, and automatically creating a new model according to the analysis result. The approach degree threshold value can be set according to actual needs, and the value of the approach degree threshold value is not specifically limited in the disclosure.

In addition, in this embodiment, for a model that cannot be matched with an existing model library, the corresponding model creation may be completed through manual intervention.

After the business data including the hotspot path information and the additional business information corresponding to the hotspot path information are processed by the business flow engine and the data flow engine, the business data are subjected to datamation storage by the two engines in a data model mode, and the user behavior can be discovered by analyzing the modeled business data.

In the embodiment, the deep learning model can be trained, and the behavior rule of the user can be pre-analyzed and found by using the deep learning model. Or the user behavior rules can be pre-analyzed and discovered through other prediction methods.

According to the embodiment of the disclosure, the algorithm used by the behavior discovery mechanism supports self-updating, so that the behavior discovery mechanism can be guaranteed to be flexible and can adapt to changes.

According to the embodiment of the disclosure, after the behavior rule of the user is found and obtained through the user behavior, some default rules of the user when using the bill can be customized based on the behavior rule of the user, such as default epilogue information and default check purpose. In addition, a timed reminding task can be set for the user based on the behavior rule of the user. For example, the user a uses the bill to pay the water and electricity fee regularly every month, the time when the user pays the water and electricity fee next time is obtained through pre-analysis of the behavior, and the user a can be reminded before the time comes, so that the reminding before the water and electricity fee is paid is realized.

According to the embodiment of the disclosure, when the behavior discovery operation is performed on the user, the service data of the user needs to be classified first.

In this embodiment, recent business data of a user is recorded in advance, and is stored in a behavior model library corresponding to the user in a data model form. The service data comprises hotspot path information and additional service information corresponding to the hotspot path information.

When the business data is classified, scanning a behavior model base corresponding to a user, and acquiring a piece of business data of the user from the behavior model base. And comparing the business data with the behavior rule data in the prediction library one by one and analyzing the correlation degree between the business data and the behavior rule data. The association degree can be obtained by comparing key elements, and the key element comparison may include, for example, money comparison, date similarity comparison, behavior purpose comparison, and the like. And if the prediction library has behavior rule data of which the association degree with the business data is higher than the association degree threshold value, classifying the behavior into a classification corresponding to the behavior rule data. And if the prediction library does not have behavior rule data of which the association degree with the service data is higher than the association degree threshold, performing new behavior rule data classification added in the prediction library according to the service data and used for expressing the behavior rule corresponding to the service data. Particularly, initially, the prediction library is empty, and after the first inventory behavior is obtained, a behavior rule data classification is newly added in the prediction library.

And repeating the operation until each piece of business data in the behavior model library is classified.

After the user service data are divided by the method, the associated service data are all included in one prediction behavior classification in the prediction library, and then each behavior rule data corresponding to the user can be intelligently screened and analyzed to find out valuable behavior rule data, and the personalized configuration data of the user is customized according to the behavior rule data.

The behavior prediction customizing method of the user prediction library can comprise the following steps: analyzing each piece of behavior rule data in the prediction library, and making equivalence judgment of each element, such as judgment of money amount, judgment of date, judgment of bill number, judgment of voucher type and other elements. And determining the screening condition according to the similar elements, for example, the money amounts of a plurality of bills of the user are the same, and the 'days' in the dates of the bills are the same, analyzing the bill service that the user processes the same money amount on the fixed date of each month in the past, and determining the user, the date, the money amount and the behavior as the screening condition. For a user meeting the screening condition, it can be predicted that the user will also process the same amount of ticket service on the next fixed date. Therefore, prompt information and prompt time can be configured for the user meeting the screening condition, and the prompt information is displayed to the user at the prompt time so as to prompt the user to handle the bill service in time. The prompting time can be before the fixed date, and the prompting information can be used for prompting the user to process the bill service.

Fig. 5 schematically shows a block diagram of an apparatus for processing ticket traffic according to an embodiment of the present disclosure.

As shown in fig. 5, theapparatus 500 for processing ticket traffic includes a receivingmodule 510 and astate transition module 520. Theapparatus 500 for processing ticket services may perform the methods described above with reference to fig. 2-3.

Specifically, the receivingmodule 510 may be configured to receive a trigger signal of the ticket service.

Thestate transition module 520 may be configured to, in case that the trigger signal of the ticket service is received, cyclically perform the state transition operation until the current state of the service transitions to the end state.

Thestate transition module 520 may include:

the obtainingsubmodule 521 may be configured to obtain a current state of the service and an action to be performed.

Thestate determination submodule 522 may be configured to determine a next state corresponding to the current state and the action to be performed by querying the state transition table.

Theexecution submodule 523 may be configured to execute the action to be executed.

Thestate setting submodule 524 may be configured to set the next state as the new current state.

According to the embodiment of the disclosure, the whole construction of the service flow engine is completed through technologies such as a state conversion table and an automaton in the whole construction process of the service flow engine, so that the service processing process is automated, and the error probability and the labor cost are reduced. In addition, the datamation of the service processing process is realized, and data support is provided for subsequent data processing.

Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.

For example, any number of the receivingmodule 510 and thestate transition module 520 may be combined in one module to be implemented, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the receivingmodule 510 and thestate transition module 520 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware by any other reasonable manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, at least one of the receivingmodule 510 and thestate transition module 520 may be at least partially implemented as a computer program module, which when executed may perform a corresponding function.

FIG. 6 schematically shows a block diagram of a computer system suitable for implementing the above described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 6 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.

As shown in fig. 6, thecomputer system 600 includes aprocessor 610, a computer-readable storage medium 620, a signal transmitter 630, and a signal receiver 640. Thecomputer system 600 may perform a method according to an embodiment of the disclosure.

In particular, theprocessor 610 may comprise, for example, a general purpose microprocessor, an instruction set processor and/or related chip set and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Theprocessor 610 may also include onboard memory for caching purposes. Theprocessor 610 may be a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.

Computer-readable storage medium 620, for example, may be a non-volatile computer-readable storage medium, specific examples including, but not limited to: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and so on.

The computer-readable storage medium 620 may include a computer program 621, which computer program 621 may include code/computer-executable instructions that, when executed by theprocessor 610, cause theprocessor 610 to perform a method according to an embodiment of the disclosure, or any variation thereof.

The computer program 621 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 621 may include one or more program modules, including 621A, 621B, … …, for example. It should be noted that the division and number of the modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, so that theprocessor 610 may execute the method according to the embodiment of the present disclosure or any variation thereof when the program modules are executed by theprocessor 610.

According to an embodiment of the present disclosure, theprocessor 610 may interact with the signal transmitter 630 and the signal receiver 640 to perform a method according to an embodiment of the present disclosure or any variation thereof.

According to an embodiment of the present invention, at least one of the receivingmodule 510 and thestate transition module 520 may be implemented as a computer program module described with reference to fig. 6, which, when executed by theprocessor 610, may implement the respective operations described above.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the 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 present 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.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method provided by the embodiments of the present disclosure, when the computer program product is run on an electronic device, the program code being adapted to cause the electronic device to implement the method of processing ticket services provided by the embodiments of the present disclosure.

The computer program, when executed by theprocessor 601, performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure. In one embodiment, the computer program may be hosted 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 a signal on a network medium, downloaded and installed through thecommunication section 609, and/or installed from theremovable medium 611. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, 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., through the internet using an internet service provider).

The flowchart 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 various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.

Claims (12)

1. A method of processing ticket traffic, comprising:

responding to the received trigger signal of the bill service, and circularly executing state conversion operation until the current state of the service is converted into an end state;

wherein the state transition operation comprises:

acquiring the current state and the action to be executed of the service;

determining a next state corresponding to the current state and the action to be executed by inquiring a state transition table;

executing the action to be executed; and

setting the next state as a new current state.

2. The method of claim 1, further comprising:

recording state transition path information corresponding to the state transition operation each time a state transition operation is performed, wherein the state transition path information at least includes the following element information: a state before state transition, a state after state transition, and an action for triggering state transition;

determining behavior rule data according to the recorded state conversion path information; and

and configuring personalized configuration data of the user according to the behavior rule data.

3. The method of claim 2, wherein determining behavior law data from the recorded state transition path information comprises:

determining hotspot path information in the recorded state conversion path information, and storing the hotspot path information to a hotspot library;

acquiring additional service information related to the hotspot path information aiming at each hotspot path information in the hotspot library; and

and determining behavior rule data according to the hotspot path information and the additional service information.

4. The method of claim 3, wherein the determining hotspot path information in the recorded state transition path information comprises:

determining the number of each type of state transition path information in the recorded state transition path information;

for each state transition path information, determining a weight of the state transition path information according to the number of the state transition path information; and

and under the condition that the weight value of the state transition path information is greater than a preset weight value threshold, determining the state transition path information as hotspot path information.

5. The method of claim 4, wherein the storing the hotspot path information to a hotspot repository comprises:

under the condition that the quantity of the hotspot path information recorded in the hotspot library does not reach the upper limit of the preset quantity, recording the hotspot path information to the hotspot library; and

and under the condition that the quantity of the hot spot path information recorded in the hot spot library reaches a preset quantity upper limit, recording the hot spot path information into the hot spot library, and deleting the hot spot path information with the lowest weight value in the hot spot library.

6. The method of claim 3, wherein the additional service information includes at least one of the following factor information: amount, user name, account number, date and currency;

the determining behavior rule data according to the hotspot path information and the additional service information includes:

and determining element information with an incidence relation in the hotspot path information and the additional service information as the behavior rule data.

7. The method of claim 2, wherein the configuring personalized configuration data of the user according to the behavior law data comprises:

acquiring behavior data of a user;

determining target behavior rule data with a similarity parameter larger than a similarity threshold value between the target behavior rule data and the behavior data of the user from the determined behavior rule data; and

and configuring the personalized configuration data of the user according to the target behavior rule data.

8. The method of claim 1, wherein the personalized configuration data comprises a prompt message and a prompt time, the method further comprising: and displaying the prompt information to the user at the prompt time.

9. An apparatus for processing ticket traffic, comprising:

the receiving module is used for receiving a trigger signal of the bill service;

the state conversion module is used for circularly executing the state conversion operation under the condition of receiving the trigger signal of the bill service until the current state of the service is converted into the end state;

wherein the state transition module comprises:

the obtaining submodule is used for obtaining the current state and the action to be executed of the service;

the state determination submodule is used for determining the next state corresponding to the current state and the action to be executed by inquiring a state conversion table;

the execution submodule is used for executing the action to be executed; and

and the state setting submodule is used for setting the next state as a new current state.

10. A computing device, comprising:

one or more processors;

a memory for storing one or more computer programs,

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

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

12. A computer program product comprising computer executable instructions for implementing the method according to any one of claims 1 to 8 when executed.

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