CN114676694A

CN114676694A - Method, device, equipment, medium and program product for generating business model

Info

Publication number: CN114676694A
Application number: CN202210308941.6A
Authority: CN
Inventors: 陈璐璐; 李森鹤; 周添翼; 叶红
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2022-03-25
Filing date: 2022-03-25
Publication date: 2022-06-28

Abstract

The utility model provides a method for generating a business model, which can be applied to the fields of cloud computing, business architecture and finance, and comprises the following steps: acquiring all component words and corresponding components of natural sentences in the demand content; matching the component words with the corresponding preset lexicon according to the components to obtain a plurality of processing frames; arranging and associating a plurality of processing frames according to the action occurrence time sequence and the association relation coefficient of each processing frame to obtain a process model; obtaining effective nouns and modified nouns in the component words, wherein the modified nouns describe the effective nouns; taking the effective noun as an entity and the modified noun as an attribute; inquiring enterprise-level information standards to obtain a threshold value corresponding to the attribute; obtaining an entity model according to the entity, the attribute and the threshold; and generating a business model according to the process model and the entity model. The present disclosure also provides a device, an apparatus, a medium, and a program product for generating a business model.

Description

Method, device, equipment, medium and program product for generating business model

Technical Field

The present disclosure relates to the field of cloud computing, the field of business architecture, and the field of finance, and in particular, to a method, an apparatus, a device, a medium, and a program product for generating a business model.

Background

Under the digital transformation wave, enterprises plan the development of the enterprises by using business models. The service model is relatively abstract, and can be normally used and constructed only after special training. In the process of business research and development under digital transformation, business requirements compiled by business personnel using natural language need to be converted into a business model so as to quickly position the technical implementation scheme of the business requirements.

In the prior art, huge manpower and material resources are usually invested, a service model is built by special workers, time consumption is long, and subsequent maintenance cost is extremely high. In the process of digital transformation, enterprises need to respond to the market in time, and therefore, too much time is hardly provided for construction and maintenance of a business model. Meanwhile, even if a service model is constructed, if the service model cannot be maintained in time, the service model will be distorted after a period of time, and cannot play an expected role any more.

In summary, in the process of constructing the existing business model, an accurate business model cannot be quickly formed, and a large amount of manpower and material resources are required to be invested.

Disclosure of Invention

The main purpose of the present disclosure is to provide a method, an apparatus, a device, a medium, and a program product for generating a business model, which are intended to solve the problem of difficult construction of the business model.

According to a first aspect of the present disclosure, there is provided a method for generating a business model, including: acquiring all component words and corresponding components of the natural sentences in the requirement content; matching the component words with the corresponding preset lexicon according to the components to obtain a plurality of processing frames; arranging and associating a plurality of processing frames according to the action occurrence time sequence and the association relation coefficient of each processing frame to obtain a process model; obtaining effective nouns and modified nouns in the component words, wherein the modified nouns describe the effective nouns; taking the effective noun as an entity and the modified noun as an attribute; inquiring enterprise-level information standards to obtain a threshold value corresponding to the attribute; obtaining an entity model according to the entity, the attribute and the threshold; and generating a business model according to the process model and the entity model.

According to the embodiment of the disclosure, the components include a subject, a predicate and an object, and the matching of the component words and the corresponding preset lexicon is performed according to the components to obtain the plurality of processing frames includes: taking the subject in each natural statement as a role to obtain a responsible person of each processing box; respectively acquiring synonyms of the predicates and the objects in the corresponding preset lexicon; after each synonym is placed in each principal, a plurality of processing boxes are obtained.

According to the embodiment of the disclosure, arranging and associating the plurality of processing frames according to the action occurrence time sequence and the association relation coefficient of each processing frame to obtain the process model comprises: generating a swimming lane corresponding to the responsible person; arranging a processing frame which has only one association relation and is associated backwards at the head; dividing each processing frame into corresponding lanes according to the action occurrence time sequence of each processing frame; and connecting the processing frames according to the time sequence to obtain a process model.

According to the embodiment of the disclosure, the natural sentence includes an unconditional sentence and a conditional sentence, and the component words are matched with the corresponding preset lexicon according to the components to obtain a plurality of processing frames, including: obtaining an action frame according to the components of the unconditional sentence; and obtaining a judgment frame according to the components of the conditional sentence.

According to the embodiment of the disclosure, the components further include a fixed language and a stateful language, and the method for generating the business model further includes: obtaining a client type according to the fixed language of the subject; obtaining the product name according to the fixed language of the object; obtaining a channel name and/or a partner name according to the scholars; respectively matching the client type, the product name, the channel name and the partner name with corresponding preset word banks to obtain corresponding model labels; model tags are added to the flow model.

According to the embodiment of the present disclosure, taking a valid noun as an entity and a modified noun as an attribute includes: matching the valid nouns with the entity model library; in the case of a match, the modified noun is taken as an attribute; and under the condition of no match, taking the effective noun as a new entity and taking the modified noun as the attribute of the new entity.

According to the embodiment of the disclosure, acquiring all the component words and corresponding components of the natural sentence in the requirement content includes: performing word segmentation and disassembly on the natural sentences line by line to obtain component words; and acquiring the components of each component word according to semantic analysis.

According to an embodiment of the present disclosure, before generating the business model according to the process model and the entity model, the method for generating the business model further includes: analyzing the correctness of the process model according to the type of the responsible person and the relevance among the processing frames; and analyzing the correctness of the entity model according to the number of the attributes.

According to an embodiment of the present disclosure, after generating the business model, the method for generating the business model further includes: and updating the business model to a business model library.

A second aspect of the present disclosure provides a generation apparatus of a business model, including: the acquisition module is used for acquiring all component words and corresponding components of the natural sentences in the required content; the flow module is used for matching the component words with the corresponding preset word bank according to the components to obtain a plurality of processing frames; arranging and associating a plurality of processing frames according to the action occurrence time sequence and the association relation coefficient of each processing frame to obtain a process model; the entity module is used for acquiring effective nouns and modified nouns in the component words, wherein the modified nouns describe the effective nouns; taking the effective noun as an entity and the modified noun as an attribute; inquiring enterprise-level information standards to obtain a threshold value corresponding to the attribute; obtaining an entity model according to the entity, the attribute and the threshold; and the generating module is used for generating a business model according to the process model and the entity model.

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

The fourth aspect of the present disclosure also provides a computer-readable storage medium, on which executable instructions are stored, and when executed by a processor, the instructions cause the processor to perform the method for generating a business model of any one of the embodiments of the present disclosure.

The fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the method of generating a business model of any of the embodiments of the present disclosure.

Drawings

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

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

FIG. 2 schematically shows a flow chart of a method of generating a business model according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow diagram of a component term acquisition method in accordance with an embodiment of the disclosure;

FIG. 4 schematically shows a flow chart of a processing block acquisition method according to an embodiment of the present disclosure;

FIG. 5 schematically illustrates a flow diagram of a flow model acquisition method according to an embodiment of the disclosure;

FIG. 6 schematically illustrates a model tag acquisition method diagram according to an embodiment of the disclosure;

FIG. 7 schematically illustrates a flowchart of a mockup element acquisition method according to an embodiment of the disclosure;

FIG. 8 schematically illustrates a flow chart of a method of model quality inspection according to an embodiment of the present disclosure;

FIG. 9 schematically illustrates a process flow diagram for an overall model building system according to an embodiment of the present disclosure;

FIG. 10 schematically illustrates a logic diagram for natural language conversion to model language in accordance with an embodiment of the disclosure;

FIG. 11 is a block diagram schematically illustrating an architecture of a business model generation apparatus according to an embodiment of the present disclosure; and

FIG. 12 schematically illustrates a block diagram of an electronic device adapted to implement a method of generating a business model in accordance with an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is 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.).

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, necessary security measures are taken, and the customs of the public order is not violated.

In the technical scheme of the disclosure, before the personal information of the user is acquired or collected, the authorization or the consent of the user is acquired.

The embodiment of the disclosure provides a method, a device, equipment, a medium and a program product for generating a business model, which can be used in the financial field or other fields. It should be noted that the method, apparatus, device, medium, and program product for generating a business model determined by the present disclosure may be used in the financial field, and may also be used in any field other than the financial field.

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

As shown in fig. 1, an application scenario 100 according to this embodiment may include

terminal devices

101, 102, 103, a network 104 and a server 105. Network 104 is the medium used to provide communication links between

terminal devices

101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

A user may use

terminal devices

101, 102, 103 to interact with a server 105 over a network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the

terminal devices

101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that the method for generating the business model provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the generating device of the business model provided by the embodiment of the present disclosure may be generally disposed in the server 105. The method for generating the service model provided by the embodiment of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the

terminal devices

101, 102, 103 and/or the server 105. Correspondingly, the generating device of the business model provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster that is different from the server 105 and can communicate with the

terminal devices

101, 102, 103 and/or the server 105.

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

The process model and the entity model in the business model are closely related to research and development work. The process model in the business model embodies each link of business processing. A complete business processing flow is started by a person due to the triggering of a certain event, and after one or more roles of a service unit provide corresponding services, a processing result is fed back to a post-personnel result triggering the event.

It should be noted that, in the service architecture, the entity model may be used to record service content of each service object, and may include information such as entity name, attribute name, and domain value; the process model may be used to represent processes of the same nature, which are grouped together in one model.

The flow model includes, for example, information in at least the following dimensions: characters, behaviors, objects, interactions, conditions, clients, products, partners, channels, and the like.

The role refers to the subject in the structure of the subject-predicate object in natural language, is an actor and is a noun word.

Behavior, which refers to a predicate in a natural language, i.e., an action, is a verbalized word.

An object is an object in a natural language, is an actor, and is a noun word.

Interaction, meaning a specific action in the natural language, such as submission, handover, notification, return, etc., is a verbalized word.

A condition refers to a condition in a conditional sentence in a natural language.

The client, the product, the partner and the channel are related elements needing to be explained in the process model and refer to noun words in the natural language.

The entity model includes, for example, at least the following dimensions of information: entity name, attribute name, domain value.

And the entity name refers to a noun word in a natural language.

And the attribute name refers to a noun word in the natural language.

The domain value is mostly an object part in a master system structure in a natural language.

The attributes in the mockup need to conform to enterprise-level data specifications, with the associated data specifications originating from a standard library of data specifications published by the enterprise.

The method for generating the business model of the disclosed embodiment will be described in detail below with fig. 2 to 10 based on the scenario described in fig. 1.

FIG. 2 schematically shows a flow chart of a method of generating a business model according to an embodiment of the disclosure.

As shown in fig. 2, the method for generating a business model of this embodiment includes, for example, steps S210 to S280, and the method for generating a business model may be executed by a business model generating apparatus.

In operation S210, all component words of the natural sentence in the requirement content and corresponding components are acquired.

In operation S220, the component words are matched with the corresponding preset lexicon according to the components, so as to obtain a plurality of processing frames.

In operation S230, a plurality of processing frames are arranged and associated according to the action occurrence timing and the association coefficient of each processing frame, so as to obtain a process model.

In operation S240, a valid noun and a modified noun in the component word are obtained, wherein the modified noun describes the valid noun.

In operation S250, the enterprise-level information criteria is queried using the valid nouns as entities and the modified nouns as attributes, resulting in thresholds corresponding to the attributes.

In operation S260, an entity model is obtained according to the entities, the attributes, and the threshold.

In operation S270, a business model is generated according to the process model and the entity model.

In the embodiment provided by the disclosure, all component words and corresponding components are extracted from a natural sentence, and then the natural sentence is matched with a preset word bank to obtain a processing frame, and the processing frame is divided into lanes to be sequenced to construct the action flows of different roles, so that construction of a flow model can be completed directly through a specification in accordance with grammar without construction by professionals. Meanwhile, by searching the effective nouns, the entities required by the entity model creation can be obtained, the corresponding attributes and thresholds can be further obtained, and the business model can be obtained by combining the process model and the entity model, so that the natural language is converted into the corresponding business model, the business model is ensured to be updated synchronously along with the business requirements, and a large amount of labor input and time occupation for building and maintaining the business model are reduced.

The sentence components, which are components, include, for example, subjects, predicates, objects, determinants, and subjects. The component words are, for example, words in respective natural sentences, and have lexical attributes such as subjects, predicates, and objects.

FIG. 3 schematically shows a flow diagram of a constituent word acquisition method according to an embodiment of the disclosure.

There are various ways to obtain word components, for example, division determination is directly performed through an input format, however, this method needs to ensure that a sentence completely conforms to a grammar, specifically, as shown in fig. 3, in this embodiment, step S210 includes:

and S211, performing word segmentation and disassembly on the natural sentences line by line to obtain component words.

S212, obtaining the components of each component word according to semantic analysis.

In natural sentences, description habits may have sentences that are not easily divided directly, such as inverted sentences. In this embodiment, complete words are directly disassembled from natural sentences through a word segmentation technology, then components of the words are obtained according to semantic analysis, and further component words are obtained, so that the applicability is stronger.

It should be noted that the word segmentation technology and semantic analysis are common in the analysis process of the natural sentence, as long as the technical effects of the word segmentation and semantic analysis can be achieved, and the selection of the specific technical program or product is not specifically limited herein.

Fig. 4 schematically shows a flowchart of a processing block acquisition method according to an embodiment of the present disclosure.

Further, as shown in fig. 4, step S220 includes, for example:

s221, the principal in each natural sentence is used as a role, and the person in charge of each processing box is obtained.

S222, respectively obtaining synonyms of the predicates and the objects in the corresponding preset lexicon.

S223, a plurality of processing boxes are obtained after placing each synonym in each person in charge.

According to an embodiment of the present disclosure, the components include, for example, a subject, a predicate, and an object. In order to form a standard flowchart, component words need to be set in a standard manner, in this embodiment, different preset word banks are set corresponding to different components, and before the processing box is established, the component words are compared with the corresponding preset word banks, for example, a subject is used as a role, the standard role bank is checked, and a synonym is found out to be a responsible person of the processing box. And comparing the verb of the predicate with a verb library of the standard service model, and finding out the synonymous standard verb to be put into a processing box. And searching entity names in the entity model library by using object nouns, and putting the entity names into verbs in the processing box if the object nouns have synonymous nouns. If no synonym exists, the subject is directly used as a person in charge of the processing box, the inquired object information is not searched, and a subsequent submission architect confirms whether the object information is deleted or not.

It is understood that when the same words as the component words exist in the preset word library, the same words are also regarded as synonyms, and the same words or the component words are used directly.

In addition, in this embodiment, when there is no synonym of a component word in the preset word library, different operations are performed according to different components, for example, direct use or submission to a subsequent architect for manual selection operation. For example, when the component word is a subject, the component word may be used as it is.

In this embodiment, each natural sentence is decomposed into a single word, and only the constituent words need to be recombined according to the sentence structure, so that the most important part of the natural sentence can be extracted, and through the main-predicate structure, the role and the action are directly obtained as objects, so as to eliminate interference and generate a standard sentence pattern, thereby facilitating the subsequent generation of the flow chart according to the sentence pattern.

Further, the natural sentence includes, for example, an unconditional sentence and a conditional sentence. Since there is a result of performing different operations according to the condition determination in the natural sentence, a determination portion needs to be added to the flow model. From the components of the unconditional sentence, an action box may be derived. A judgment frame and an action frame can be obtained according to the components of the conditional sentence. The conditions of a conditional sentence appearing in the natural language are put into a judgment frame, the predicates and the objects are analyzed to form an action frame for the content after the conditional sentence, and the association relationship between the judgment frame and the action frame is directly established.

Fig. 5 schematically shows a flowchart of a flow model acquisition method according to an embodiment of the present disclosure.

Further, as shown in fig. 5, step S230 includes, for example:

s231, generating a lane corresponding to the person in charge.

S232, arranging the processing frame which has only one association relation and is associated backwards at the head.

S233, the processing frames are arranged in the corresponding lanes according to the operation occurrence timing of the processing frames.

And S234, connecting the processing frames according to time sequence to obtain a process model.

According to the embodiment of the present disclosure, the responsible persons of all processing boxes are extracted, different lanes are drawn respectively, and the responsible persons are used as roles to define the different lanes. The processing blocks include, for example, action blocks and decision blocks. The action frame has at least one incidence relation and at most two incidence relations. The judgment frame has at least three incidence relations: one inflow and at least two outflows. When a processing block has only one association relationship, the processing block is the first or last processing block of the flow model. The person in charge who has only one association relationship and is the processing box associated to the back is taken as the role of the first swim lane. And sequencing the lanes according to the incidence relation of the processing frame/the judgment frame and the appearing sequence. For the role with repeated appearance, the first appearance is taken as the standard. The processing box for which the person is responsible is placed in its lane, by person in charge. And drawing the connecting line of each processing frame/judgment frame according to the incidence relation of the processing frames/judgment frames, namely the time sequence of the action of each processing frame to obtain the process model.

It can be understood that according to the 'interactive' verb, two directly related roles and actions thereof can be retrieved and mapped to corresponding processing boxes, and the association relationship between the processing boxes/judgment boxes can be established.

Fig. 6 schematically shows a model tag acquisition method diagram according to an embodiment of the present disclosure.

Further, the components include, for example, a fixed phrase and a idiom. In order to describe the flow model more finely, dimension information such as clients, products, partners and channels can be supplemented by introducing relevant labels. As shown in fig. 6, the method for generating a business model further includes:

and S235, obtaining the client type according to the fixed language of the subject.

And S236, obtaining the product name according to the fixed language of the object.

And S237, obtaining the channel name and/or the partner name according to the shape language.

And S238, matching the client type, the product name, the channel name and the partner name with the corresponding preset word stock to obtain corresponding model labels.

And S239, adding each model label to the process model.

According to the embodiment of the disclosure, existing information of clients, products, channels and partners is queried by using fixed languages and place-like languages, and the queried result is put into corresponding information. And if the information is not inquired, the subsequent submission architect confirms whether the information is deleted. The fixed language and the idiom are used for describing nouns in natural sentences, tag words are formed by extracting the fixed language and the idiom, and the tag words are annotated in the model tags after the process model is generated, so that the follow-up comparison and matching with the corresponding information base are facilitated.

Fig. 7 schematically shows a flowchart of a mockup element acquisition method according to an embodiment of the disclosure.

Further, as shown in fig. 7, elements for constructing the solid model are acquired, for example, by steps S251 to S253.

And S251, matching the effective nouns with the entity model library.

S252, in the case of matching, takes the modified noun as the attribute.

And S253, under the condition of no match, taking the effective noun as a new entity and taking the modified noun as the attribute of the new entity.

In the present embodiment, the valid noun is, for example, a noun having a noun word or sentence description of the noun. And extracting nouns in each sentence in the requirement described by the natural language and nominal words and sentences for describing the nouns by using the word segmentation and semantic analysis technology. And classifying and storing the noun sentences describing the same noun as effective nouns. A noun is deleted if it does not have the word or sentence that describes it.

Specifically, the extracted nouns can be matched with nouns existing in the entity model library, and matching can be performed according to similar words. If the noun or its similar meaning word exists in the library, the noun describing the noun is used as the attribute, and the word is pressed for processing. If there is no noun or its similar words in the library, the noun is used as the name and key attribute of the new entity, the core noun in the sentence describing the noun is used as the attribute of the new entity, and the related description is used as the attribute description. The entity model library is supplemented by acquiring effective nouns, the effective nouns are used as entity names, the fixed term nouns and the stateful term nouns which describe the effective nouns are used as attributes, and enterprise standards are supplemented as thresholds, so that the entity model library can be updated, and the completeness and correctness of data of the constructed entity model library are guaranteed.

It should be noted that, if there is an acquired valid noun or its synonym in the library, the corresponding word can be directly taken out from the library as a new valid noun.

In addition, if the corresponding obtained attributes are less than the preset number, subsequent supplementation is performed by architects, or the effective names are combined with similar words in a preset word stock. For example, when the number of non-critical attributes of the newly added entity is less than 3, the entity which cannot exist independently is forwarded to the architect for analysis, and either the attributes are supplemented or the attributes are integrated into the existing entity.

FIG. 8 schematically shows a flow chart of a model quality inspection method according to an embodiment of the present disclosure.

According to the embodiment of the present disclosure, in order to improve the quality of model construction, before generating a business model from a process model and a solid model, as shown in fig. 8, the correctness of the process model and the solid model are respectively checked, for example, through steps S271 to S272.

And S271, analyzing the correctness of the process model according to the type of the responsible person and the relevance among the processing frames.

According to an embodiment of the present disclosure, the processing block includes, for example, an action block and a decision block. The action frame has at least one incidence relation and at most two incidence relations. The judgment frame has at least three incidence relations: one inflow and at least two outflows. At least one of the roles is non-system. At least two outlets of the judgment frame are provided. There are no function blocks that are not associated with other function blocks. There are multiple contents in the client, product, channel and cooperation box in the same business model. According to the principle, the correctness of the association relation and the integrity of the service requirement can be analyzed. And checking whether all the processing frames are constructed correctly or not, and avoiding the processing frames which are not added into the flow model or the processing frames with wrong labels in sequence.

And S272, analyzing the correctness of the entity model according to the number of the attributes.

According to embodiments of the present disclosure, when, for example, the number of non-critical attributes of the newly added entity is less than 3, the architect analysis is passed on as an entity that cannot exist alone, either supplementing the attributes or integrating into an existing entity. And when the attributes of the newly added and modified entity model are less than 5, the entity model is regarded as not meeting the standard requirement.

Furthermore, in order to improve the real-time performance of the business model, the method for generating the business model further includes:

and S280, updating the business model to a business model library when the specified condition is met.

In the embodiment, the generation of the business model flow chart under various conditions can be adapted by updating the preset word bank as required. There are various ways to update the preset lexicon, for example, directly adding by architects, in this embodiment, different implementation ways are selected according to different components of the preset lexicon, and when the preset lexicon is a main lexicon whose components are main words, it is only necessary to directly add the component words that are not matched to the preset lexicon. And the real-time updating of the business model base can be realized by updating each preset word base.

The predetermined condition includes, for example, after the operation reaches a certain time, after a new service line is started, or after the matching fails, so long as the matching degree of the natural language can be ensured.

FIG. 9 schematically shows a process flow diagram of an overall model building system according to an embodiment of the present disclosure.

As shown in fig. 9, in a general view, after a user submits a requirement specification written in a natural language, the business model building system converts the natural language into a business model and performs a model compliance check, updates the business model into a model base when the compliance check is performed, and submits the business model to an architect for uniform confirmation or adjustment and then updates the business model into the model base when the compliance check is not performed.

FIG. 10 schematically illustrates a logic diagram for natural language conversion to model language in accordance with an embodiment of the disclosure.

As shown in fig. 10, the respective components in the sentence can be obtained by analyzing the sentence in the natural language as a whole. Then, the flow model can be constructed by using the conditions of the subject predicate object and the condition sentence, and the flow model can be supplemented by using the subject's fixed language, the object's fixed language and the place shape to obtain a client label, a product label, a channel label, a partner label and the like, so that the construction logic of a complete flow model is embodied.

Based on the above method for generating a flow model, the present disclosure provides a specific embodiment:

requirement description:

(title) an individual customer transacts a study-leaving remittance service at a personal cell phone bank.

(required content) after the individual customer clicks on the study remittance in the mobile phone bank, the system pops up the page of the study remittance for the customer to fill out. The customer selects the identity document, and after the number is input, the system checks the identity document through the network of the public security department system. When the certificate information elements are not matched, the system prompts that the certificate provided by a user can not be transacted in a mobile phone bank temporarily and please transact in a website, and a client clicks the confirmation page to return to a remittance service page; when the certificate information elements are consistent, the customer can input information such as school names.

(subsequent processing omission)

First, basic component words and corresponding components are extracted from the demand content.

The subject: client and system.

Both of them are preset word stock, i.e. standard role stock content, and do not need to be adjusted. If no synonym exists, for example, no client exists in the character library, the extracted component words are directly used as component words. I.e. directly with the customer as a component word.

Verb: clicking, popping up, selecting, inputting, checking, prompting, returning and inputting. These verbs are, for example, standard verbs and do not require adjustment.

The object is: remittance of study, page, identity document, number.

Language determination: personal, study-left remittance, networking.

In this embodiment, after matching with the current customer, product, and partner information bases, the person is filled in the customer label of the flow model, and the study-leaving remittance is filled in the product label of the flow model. Networking does not exist in a relevant information base, and whether deletion is carried out or not is confirmed by an architect subsequently.

Place shape language: personal mobile banking, department of public security system.

And after matching with the current client, product and partner information base, filling the personal mobile phone bank in the channel label of the flow model. The public security department system is not in the related information base and submits the confirmation of the architect.

Conditional sentence: when the certificate information elements are not matched, the system prompts that the certificate provided by you can not be transacted in the mobile phone bank temporarily and please transact in a website, and the customer clicks the confirmation and then returns the page to the remittance service page. When the information is matched, the customer can input information such as school name and the like.

And combining action frames. And combining the component words to obtain an action frame [ ]. (customer) [ click on remittance for study ], (system) [ pop-up page ], (customer) [ select identity document ], (customer) [ enter number ], (system) web check + identity document and number ].

And acquiring a judgment frame. And extracting conditions in the condition sentences to obtain a judgment frame '< >', and combining component words of the condition sentences to obtain an action frame '[ ]' directly related to the judgment frame '< >'. < certificate element matching > (client) [ enter school name ], < certificate element not matching > (system) [ prompt "you provided certificate can't be transacted in mobile phone bank temporarily, please go to website transact" ].

And secondly, establishing the relation in the model. And sorting according to the text description sequence.

1- (client) [ click on remittance for leaving school ], 2- (system) [ pop-up page ], 3- (client) [ select identity document ], 4- (client) [ enter number ], 5- (system) [ check + identity document and number ], 6- < document element is consistent > (client) [ enter school name ], < document element is not consistent > (system) [ prompt you provided document can't be transacted on mobile phone bank temporarily, please go to website transact "].

And thirdly, drawing a process model.

Draw lanes by role, resulting in two lanes: one is the client and one is the system.

The client and system are put into the roles of swim lanes, respectively.

And putting the action frames with the results of the client obtained in the second step into the swim lane with the client role in sequence, and putting the action frames with the results of the system and the judgment frames into the swim lane with the system role in sequence.

And drawing connecting lines among the action frames according to the sequence obtained by the analysis in the second step. "< >" has two meanings, but the meanings are opposite, for example, go to negative meaning, leave < document element does not conform >, followed by corresponding action box. For example, positive and negative words may be embodied on a line connecting "< >" and "[ ]".

And filling the labels of the client, the product, the channel and the partner determined in the second step into the label of the process model.

After the flow chart is drawn, submitting to an architect for verification, and prompting a system of a networking department and a public security department, non-clients, products, channels and partners according to the label to confirm whether to adjust the model or not. "the architect performs the correlation process in the system.

In particular, the present disclosure provides embodiments for updating a mockup library.

The service requirement specification has a section of new electronic identity card with the content of 'certificate type'. The user can handle the relevant business by the electronic identity card.

First, the nouns are analyzed. By using word segmentation technique and semantic analysis technique, it can be obtained that the initial noun of the first sentence is the 'identity document'. The initial noun of the second sentence is user, ID card, service. For example, if there is no sentence describing the user or the service in the entire paragraph, the user or the service is deleted. If there are two sentences described by the name words of the identity card, the name words are valid name words.

Second, the valid nouns are searched in the existing database, where the nouns are present in the existing entity. The definite language of the noun is picked up and the near-meaning word matching is performed in the entity. The definite language of the identity card is 'electronic'. If not found in the existing entity, then "electronic" is added as an attribute to the existing entity.

And thirdly, supplementing the threshold value. And supplementing the domain value of the enterprise-level information standard into the domain value corresponding to the attribute of the electronic identity card. And if the enterprise-level information standard has no relevant domain value, the enterprise-level information standard is null and submitted to the architect for supplement.

In summary, the present disclosure provides a method for generating a service model, which converts a natural language into a corresponding service model through multidimensional analysis of the natural language, meets the requirement of reflecting services from different dimensions, ensures that the service model is updated synchronously with service requirements, and reduces a large amount of human input and time occupation for constructing and maintaining the service model.

Based on the method for generating the business model, another aspect of the present disclosure provides a device for generating the business model. The apparatus will be described in detail below with reference to fig. 11.

Fig. 11 schematically shows a block diagram of a generating apparatus of a business model according to an embodiment of the present disclosure.

As shown in fig. 11, another aspect of the present disclosure provides a device 1100 for generating a business model, for example, including: an acquisition module 1110, a flow module 1120, an entity module 1130, and a generation module 1140.

The obtaining module 1110 is configured to obtain all component words and corresponding components of the natural language sentence in the content of the requirement. In an embodiment, the obtaining module 1110 may be configured to perform the operation S210 described above, which is not described herein again.

The process module 1120 is configured to match the component words with the corresponding preset lexicon according to the components to obtain a plurality of processing frames, and arrange and associate the plurality of processing frames according to the action occurrence time sequence and the association relation coefficient of each processing frame to obtain a process model. In an embodiment, the flow module 1120 may be configured to perform the operations S220 to S230 described above, which are not described herein again.

The entity module 1130 is configured to obtain an effective noun and a modified noun in a component word, where the modified noun describes the effective noun, perform database search on the effective noun, use the effective noun as an entity, use the modified noun as an attribute, query an enterprise-level information standard to obtain a threshold corresponding to the attribute, and obtain an entity model according to the entity, the attribute, and the threshold. In an embodiment, the entity module 1130 may be configured to perform the operations S240 to S260 described above, which are not described herein again.

The generation module 1140 is used to generate a business model from the process model and the entity model. In an embodiment, the generating module 1140 may be configured to perform the operation S270 described above, which is not described herein again.

Any number of the obtaining module 1110, the flow module 1120, the entity module 1130, and the generating module 1140 may be combined in one module or any one of them may be split into multiple modules according to an embodiment of the present disclosure. 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 obtaining module 1110, the flow module 1120, the entity module 1130, and the generating module 1140 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 hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or may be implemented in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the obtaining module 1110, the flow module 1120, the entity module 1130, and the generating module 1140 may be implemented at least in part as a computer program module that, when executed, may perform corresponding functions.

As shown in fig. 12, an electronic apparatus 1200 according to an embodiment of the present disclosure includes a processor 1201, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)1202 or a program loaded from a storage section 1208 into a Random Access Memory (RAM) 1203. The processor 1201 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 1201 may also include on-board memory for caching purposes. The processor 1201 may include a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the present disclosure.

In the RAM 1203, various programs and data necessary for the operation of the electronic apparatus 1200 are stored. The processor 1201, the ROM 1202, and the RAM 1203 are connected to each other by a bus 1204. The processor 1201 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 1202 and/or the RAM 1203. Note that the programs may also be stored in one or more memories other than the ROM 1202 and the RAM 1203. The processor 1201 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

Electronic device 1200 may also include input/output (I/O) interface 1205, according to an embodiment of the disclosure, input/output (I/O) interface 1205 also connected to bus 1204. The electronic device 1200 may also include one or more of the following components connected to the I/O interface 1205: an input section 1206 including a keyboard, a mouse, and the like; an output portion 1207 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 1208 including a hard disk and the like; and a communication section 1209 including a network interface card such as a LAN card, a modem, or the like. The communication section 1209 performs communication processing via a network such as the internet. A driver 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is mounted on the drive 1210 as necessary, so that a computer program read out therefrom is mounted into the storage section 1208 as necessary.

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. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 1202 and/or the RAM 1203 and/or one or more memories other than the ROM 1202 and the RAM 1203 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the generation method of the business model provided by the embodiment of the disclosure.

The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 1201. The above described systems, devices, modules, units, etc. 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 the communication section 1209, and/or installed from the removable medium 1211. 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 such an embodiment, the computer program can be downloaded and installed from a network through the communication portion 1209 and/or installed from the removable medium 1211. The computer program, when executed by the processor 1201, performs the above-described functions defined in the system of the embodiments of the present disclosure. The above described systems, devices, apparatuses, modules, units, etc. may be implemented by computer program modules according to embodiments of the present disclosure.

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's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of 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 that 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.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

The above-mentioned embodiments are intended to illustrate the objects, aspects and advantages of the present disclosure in further detail, and it should be understood that the above-mentioned embodiments are only illustrative of the present disclosure and are not intended to limit the present disclosure, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.

Claims

1. A method for generating a business model, comprising:

acquiring all component words and corresponding components of the natural sentences in the requirement content;

matching the component words with a corresponding preset word bank according to the components to obtain a plurality of processing frames;

arranging and relating the processing frames according to the action occurrence time sequence and the correlation coefficient of each processing frame to obtain a process model;

obtaining a valid noun and a modified noun in the component word, wherein the modified noun describes the valid noun;

taking the effective noun as an entity and the modified noun as an attribute;

inquiring enterprise-level information standard to obtain a threshold value corresponding to the attribute;

obtaining an entity model according to the entity, the attribute and the threshold;

and generating a business model according to the process model and the entity model.

2. The method for generating a business model according to claim 1, wherein the components include a subject, a predicate and an object, and the matching the component words with the corresponding predetermined lexicon according to the components to obtain the plurality of processing boxes includes:

taking the subject in each natural statement as a role to obtain a responsible person of each processing box;

respectively acquiring synonyms of the predicates and the objects in the corresponding preset lexicon;

and placing each synonym in each person in charge to obtain a plurality of processing boxes.

3. The method of generating a business model according to claim 2, wherein the arranging and associating the plurality of processing blocks according to the action occurrence timing sequence and the association relation coefficient of each processing block to obtain the process model includes:

generating a swim lane corresponding to the responsible person;

arranging the processing frames which have only one association relationship and are associated backwards at the head;

dividing each processing frame into corresponding lanes according to the action occurrence time sequence of each processing frame;

and connecting the processing frames according to the time sequence to obtain the process model.

4. The method for generating a business model according to claim 1, wherein the natural sentences include unconditional sentences and conditional sentences, and the matching of the component words with the corresponding preset lexicon according to the components to obtain the plurality of processing frames includes:

obtaining an action frame according to the components of the unconditional sentence;

and obtaining a judgment frame according to the components of the conditional sentence.

5. The method of generating a business model of claim 2, wherein the components further include a fixed phrase and a stateful phrase, the method of generating a business model further comprising:

obtaining a client type according to the fixed language of the subject;

obtaining the product name according to the fixed language of the object;

obtaining a channel name and/or a partner name according to the shape language;

respectively matching the customer type, the product name, the channel name and the partner name with corresponding preset word banks to obtain corresponding model labels;

adding each of the model tags to the flow model.

6. The method of generating a business model according to claim 1, wherein the using the valid noun as an entity and the modified noun as an attribute comprises:

matching the valid nouns with a solid model library;

in case of a match, taking the modified noun as an attribute;

and in the case of no match, taking the effective noun as a new entity, and taking the modified noun as the attribute of the new entity.

7. The method of generating a business model according to claim 1, wherein the obtaining all component words and corresponding components of the natural language sentence in the requirement content comprises:

performing word segmentation and disassembly on the natural sentences line by line to obtain the component words;

and acquiring the components of each component word according to semantic analysis.

8. The method of generating business models of claim 2, wherein prior to generating business models from the process model and the entity model, the method of generating business models further comprises:

analyzing the correctness of the process model according to the type of the responsible person and the relevance between the processing frames;

and analyzing the correctness of the entity model according to the number of the attributes.

9. The method for generating a business model of claim 8, wherein after generating the business model, the method for generating a business model further comprises:

and updating the business model to a business model library.

10. An apparatus for generating a business model, comprising:

the acquisition module is used for acquiring all component words and corresponding components of the natural sentences in the required content;

the flow module is used for matching the component words with the corresponding preset word bank according to the components to obtain a plurality of processing frames; arranging and relating the processing frames according to the action occurrence time sequence and the correlation coefficient of each processing frame to obtain a process model;

the entity module is used for acquiring effective nouns and modified nouns in the component words, wherein the modified nouns describe the effective nouns; taking the effective noun as an entity and the modified noun as an attribute; inquiring enterprise-level information standards to obtain a threshold value corresponding to the attribute; obtaining an entity model according to the entity, the attribute and the threshold; and the number of the first and second groups,

and the generating module is used for generating a business model according to the process model and the entity model.

11. An electronic device, comprising:

one or more processors;

a storage device for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of generating a business model of any of claims 1-9.

12. A computer-readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform a method of generating a business model in accordance with any one of claims 1 to 9.

13. A computer program product, characterized in that it comprises a computer program which, when being executed by a processor, implements the method of generating a business model according to any one of claims 1 to 9.