Business model generation method, device, equipment, medium and program product

Abstract

The present disclosure provides a method for generating a business model, which can be applied to the fields of cloud computing, business architecture, and finance. The method includes: obtaining all component words and corresponding components of natural sentences in the demand content; matching the component words with the corresponding preset word library according to the components to obtain multiple processing boxes; arranging and associating the multiple processing boxes according to the action occurrence sequence and the number of association relationships of each processing box to obtain a process model; obtaining valid nouns and modified nouns in the component words, wherein the modified nouns describe the valid nouns; taking the valid nouns as entities and the modified nouns as attributes; querying enterprise-level information standards to obtain thresholds corresponding to the attributes; obtaining an entity model according to the entity, attribute, and threshold; generating a business model according to the process model and the entity model. The present disclosure also provides a business model generation device, equipment, medium, and program product.

Description

Technical Field

[0001] The present disclosure relates to the fields of cloud computing, business architecture and finance, and in particular to a method, device, equipment, medium and program product for generating a business model. Background Art

[0002] Under the wave of digital transformation, enterprises are planning their business development with business models. Business models are relatively abstract and require special training before they can be used and built normally. In the process of business development under digital transformation, business requirements written by business personnel in natural language need to be converted into business models to quickly locate technical implementation plans for business requirements.

[0003] In the existing technology, it is often necessary to invest huge manpower and material resources, and specialized staff often build business models, which is time-consuming and has extremely high subsequent maintenance costs. In the process of digital transformation, enterprises need to respond to the market in a timely manner, and it is difficult to provide too much time for the construction and maintenance of business models. At the same time, even if a business model is built, if it is not maintained in a timely manner, it will be distorted after a period of time and will no longer play the expected role.

[0004] In summary, in the existing business model construction process, it is impossible to quickly form an accurate business model, which requires a lot of manpower and material resources. Summary of the invention

[0005] The main purpose of the present disclosure is to propose a method, device, equipment, medium and program product for generating a business model, aiming to solve the problem of difficulty in constructing a business model.

[0006] According to a first aspect of the present disclosure, a method for generating a business model is provided, comprising: obtaining all component words and corresponding components of natural sentences in demand content; matching the component words with corresponding preset vocabulary according to the components to obtain multiple processing boxes; arranging and associating the multiple processing boxes according to the action occurrence sequence and the number of association relationships of each processing box to obtain a process model; obtaining valid nouns and modified nouns in the component words, wherein the modified nouns describe the valid nouns; taking the valid nouns as entities and the modified nouns as attributes; querying enterprise-level information standards to obtain thresholds corresponding to the attributes; obtaining an entity model according to the entities, attributes and thresholds; and generating a business model according to the process model and the entity model.

[0007] According to an embodiment of the present disclosure, components include a subject, a predicate and an object. According to the components, the component words are matched with the corresponding preset vocabulary to obtain multiple processing boxes including: taking the subject in each natural sentence as a role to obtain the person in charge of each processing box; respectively obtaining the synonyms of each predicate and each object in the corresponding preset vocabulary; placing each synonym after each person in charge to obtain multiple processing boxes.

[0008] According to an embodiment of the present disclosure, multiple processing boxes are arranged and associated according to the sequence of action occurrence and the number of association relationships of each processing box, and a process model is obtained, including: generating lanes for corresponding persons in charge; arranging the processing box with only one association relationship and backward association at the first place; according to the sequence of action occurrence of each processing box, each processing box is divided into a corresponding lane; and each processing box is connected in sequence to obtain a process model.

[0009] According to an embodiment of the present disclosure, natural sentences include non-conditional sentences and conditional sentences. According to the components, the component words are matched with the corresponding preset vocabulary to obtain multiple processing boxes including: obtaining an action box according to the components of the non-conditional sentence; obtaining a judgment box according to the components of the conditional sentence.

[0010] According to an embodiment of the present disclosure, the components also include attributives and adverbials, and the method for generating a business model also includes: obtaining a customer type based on the attributive of the subject; obtaining a product name based on the attributive of the object; obtaining a channel name and / or a partner name based on the adverbial; respectively matching the customer type, product name, channel name and partner name with the corresponding preset vocabulary to obtain corresponding model labels; and adding each model label to the process model.

[0011] According to an embodiment of the present disclosure, taking effective nouns as entities and taking modified nouns as attributes includes: matching effective nouns with entity model libraries; in case of a match, taking modified nouns as attributes; in case of a mismatch, taking effective nouns as newly added entities and taking modified nouns as attributes of the newly added entities.

[0012] According to an embodiment of the present disclosure, obtaining all the constituent words and corresponding components of a natural sentence in the demand content includes: decomposing the natural sentence line by line to obtain the constituent words; and obtaining the components of each constituent word based on semantic analysis.

[0013] According to an embodiment of the present disclosure, before generating a business model based on a process model and an entity model, the method for generating a business model further includes: analyzing the correctness of the process model based on the type of person in charge and the correlation between each processing box; and analyzing the correctness of the entity model based on the number of attributes.

[0014] According to an embodiment of the present disclosure, after generating the business model, the method for generating the business model further includes: updating the business model to a business model library.

[0015] The second aspect of the present disclosure provides a device for generating a business model, including: an acquisition module, used to acquire all component words and corresponding components of natural sentences in the demand content; a process module, used to match the component words with corresponding preset vocabulary according to the components to obtain multiple processing boxes; arrange and associate the multiple processing boxes according to the action occurrence sequence and the number of association relationships of each processing box to obtain a process model; an entity module, used to acquire valid nouns and modified nouns in the component words, wherein the modified nouns describe the valid nouns; take the valid nouns as entities and the modified nouns as attributes; query the enterprise-level information standard to obtain the threshold value corresponding to the attribute; obtain the entity model according to the entity, the attribute and the threshold value; and a generation module, used to generate a business model according to the process model and the entity model.

[0016] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the method for generating a business model of any embodiment of the present disclosure.

[0017] The fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method for generating a business model of any embodiment of the present disclosure.

[0018] The fifth aspect of the present disclosure further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for generating a business model of any embodiment of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above contents and other purposes, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0020] Figure 1 The application scenario diagram schematically shows the method, apparatus, device, medium and program product for generating a business model according to an embodiment of the present disclosure;

[0021] Figure 2 A flowchart of a method for generating a business model according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 3 A flow chart of a component word acquisition method according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 4 A flowchart of a processing frame acquisition method according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 5A flowchart of a method for obtaining a process model according to an embodiment of the present disclosure is schematically shown;

[0025] Figure 6 A diagram schematically shows a method for obtaining a model label according to an embodiment of the present disclosure;

[0026] Figure 7 A flow chart of a method for acquiring entity model elements according to an embodiment of the present disclosure is schematically shown;

[0027] Figure 8 A flow chart of a model quality inspection method according to an embodiment of the present disclosure is schematically shown;

[0028] Fig. 9 The process flow chart of the entire model building system according to the embodiment of the present disclosure is schematically shown;

[0029] Fig.10 A logic diagram of converting a natural language into a model language according to an embodiment of the present disclosure is schematically shown;

[0030] Fig.11 A structural block diagram schematically shows a device for generating a business model according to an embodiment of the present disclosure; and

[0031] Fig.12 A block diagram of an electronic device suitable for implementing a method for generating a business model according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0032] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0033] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0034] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0035] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0036] In the technical solution disclosed in the present invention, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations, necessary confidentiality measures are taken, and do not violate public order and good morals.

[0037] In the technical solution of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0038] The embodiments of the present disclosure provide a method, device, equipment, medium and 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, device, equipment, medium and program product for generating a business model determined in the present disclosure can be used in the financial field, and can also be used in any field other than the financial field, and the application field of the method, device, equipment, medium and program product for generating a business model disclosed in the present disclosure is not limited.

[0039] Figure 1 An application scenario diagram of a method, apparatus, device, medium, and program product for generating a business model according to an embodiment of the present disclosure is schematically shown.

[0040] like Figure 1 As shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0041] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

[0042] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0043] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0044] It should be noted that the method for generating the business model provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the device for generating the business model provided in the embodiment of the present disclosure can generally be set in the server 105. The method for generating the business model provided in the embodiment of the present disclosure can also be executed by 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. Accordingly, the device for generating the business model provided in the embodiment of the present disclosure can also be set 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.

[0045] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0046] The process model and entity model in the business model are closely related to R&D work. The process model in the business model reflects each link of business processing. A complete business processing process is initiated by someone due to something, and after one or more roles of the service unit provide corresponding services, the processing results are fed back to the person who triggered the event.

[0047] It should be noted that in the business architecture, the entity model can be used to record the business content of each business object, which can include information such as entity name, attribute name, domain value, etc.; the process model can be used to represent processes of the same nature, and they are classified together into one model.

[0048] The process model includes, for example, information on at least the following dimensions: roles, behaviors, objects, interactions, conditions, customers, products, partners, channels, and the like.

[0049] Role refers to the subject in the subject-predicate-object structure in natural language. It is the agent and a noun word.

[0050] Behavior refers to the predicate in natural language, that is, action, which is a verb word.

[0051] Object refers to the object in natural language, which is the patient and a noun.

[0052] Interaction refers to specialized actions in natural language, such as submission, transfer, notification, return, etc., which is a verb word.

[0053] Condition refers to the condition in a conditional sentence in natural language.

[0054] Customers, products, partners, and channels are related elements that need to be explained in the process model, and refer to noun words in natural language.

[0055] The entity model, for example, includes at least the following dimensional information: entity name, attribute name, and domain value.

[0056] Entity name refers to a noun word in natural language.

[0057] Attribute name refers to a noun word in natural language.

[0058] Domain values ​​are usually the object part of the subject-copula-table structure in natural language.

[0059] Each attribute in the entity model needs to comply with enterprise-level data specifications, and the relevant data specifications are derived from the data specification standard library released by the enterprise.

[0060] The following will be based on Figure 1 The scene described by Figure 2 to Figure 10 The method for generating the business model of the disclosed embodiment is described in detail.

[0061] Figure 2 The flowchart of the method for generating a business model according to an embodiment of the present disclosure is schematically shown.

[0062] like Figure 2 As shown, 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 can be executed by a business model generating device.

[0063] In operation S210, all component words and corresponding components of the natural sentence in the demand content are obtained.

[0064] In operation S220, the component words are matched with corresponding preset vocabulary according to the components to obtain a plurality of processing boxes.

[0065] In operation S230 , multiple processing blocks are arranged and associated according to the action occurrence sequence and the number of association relationships of each processing block to obtain a process model.

[0066] In operation S240, effective nouns and modified nouns in the component words are acquired, wherein the modified nouns describe the effective nouns.

[0067] In operation S250, the effective noun is taken as an entity, the modified noun is taken as an attribute, and the enterprise-level information standard is queried to obtain a threshold value corresponding to the attribute.

[0068] In operation S260 , an entity model is obtained according to the entity, the attribute, and the threshold.

[0069] In operation S270 , a business model is generated according to the process model and the entity model.

[0070] In the embodiment provided by the present disclosure, all the component words and corresponding components are first extracted from the natural sentence, and then matched with the preset word library to obtain the processing box. By sorting the processing box by lanes, the action flow of different roles is constructed. No professional personnel are required to construct it, and the construction of the process model can be completed directly through the grammatical instructions. At the same time, by searching for valid nouns, the entities required to create the entity model can be obtained, and then the corresponding attributes and thresholds can be obtained. The business model can be obtained by combining the process model and the entity model, realizing the conversion of natural language into the corresponding business model, ensuring that the business model is updated synchronously with business needs, and reducing the large amount of manpower investment and time occupied in building and maintaining the business model.

[0071] It should be noted that the components are sentence components, including, for example, subject, predicate, object, attributive, adverbial, etc. Component words are, for example, words in each natural sentence, and have lexical attributes such as subject, predicate, object, etc.

[0072] Figure 3 The flowchart of the component word acquisition method according to the embodiment of the present disclosure is schematically shown.

[0073] There are many ways to obtain word components, such as directly dividing and judging by the input format. However, this method requires that the sentence is completely grammatical. Specifically, Figure 3 As shown, in this embodiment, step S210 includes, for example:

[0074] S211, segmenting and decomposing the natural sentence line by line to obtain component words.

[0075] S212, obtaining the components of each component word according to semantic analysis.

[0076] In natural sentences, description habits may appear in inversion and other sentence patterns that are not easy to be directly divided. In this embodiment, the complete words are directly disassembled from the natural sentences through word segmentation technology, and then the components of the words are obtained according to semantic analysis, and then the component words are obtained, which is more applicable.

[0077] It should be noted that word segmentation technology and semantic analysis are more common in the analysis process of natural sentences. As long as the technical effects of word segmentation and semantic analysis can be achieved, the selection of specific technical procedures or products is not specifically restricted here.

[0078] Figure 4 A flowchart of a processing frame acquisition method according to an embodiment of the present disclosure is schematically shown.

[0079] Furthermore, if Figure 4 As shown, step S220 includes, for example:

[0080] S221, taking the subject in each natural sentence as a role, obtain the person in charge of each processing box.

[0081] S222, respectively obtain synonyms of each predicate and each object in the corresponding preset vocabulary.

[0082] S223, placing each synonym after each person in charge to obtain multiple processing boxes.

[0083] According to an embodiment of the present disclosure, the components include, for example, a subject, a predicate, and an object. In order to facilitate the formation of a standard flow chart, it is necessary to standardize the setting of the component words. In this embodiment, different preset word libraries are set corresponding to different components. Before establishing a processing box, the component words are first compared with the corresponding preset word library. For example, the subject is used as a role, and the standard role library is checked to find synonyms as the person in charge of the processing box. The predicate verb is compared with the verb library of the standard business model, and the synonymous standard verbs are found and put into the processing box. In addition, the object noun is used to search for the entity name in the entity model library. If there is a synonym noun, the entity name is put after the verb in the processing box. If there is no synonym, the subject is directly used as the person in charge of the processing box. The object information that has not been queried will be submitted to the architect to confirm whether to delete it later.

[0084] It is understandable that when there are words identical to component words in the preset vocabulary, the identical words are also regarded as synonyms, and the identical words or component words can be directly used.

[0085] In addition, in this embodiment, when there is no synonym for a component word in the preset word library, different operations are performed according to different components, such as directly using or submitting to a subsequent architect for manual selection. For example, when a component word is a subject, the component word can be directly used.

[0086] In this embodiment, since each natural sentence is broken down into individual words, the most important part of the natural sentence can be extracted by only recombining the component words according to the sentence structure. Through the subject-predicate-object structure, the role, action and object can be directly obtained, interference is eliminated, and a standard sentence pattern is generated, which is convenient for the subsequent generation of a flowchart based on the sentence pattern.

[0087] Furthermore, natural sentences include, for example, non-conditional sentences and conditional sentences. Since there are results of different operations based on conditional judgments in natural sentences, it is necessary to add a judgment part to the process model. According to the components of non-conditional sentences, an action box can be obtained. According to the components of conditional sentences, a judgment box and an action box can be obtained. For conditional sentences appearing in natural language, put their conditions into the judgment box, analyze the content after the conditional sentence to obtain the predicate and object to form an action box, and directly establish an association relationship between the judgment box and the action box.

[0088] Figure 5 The flowchart of the process model acquisition method according to the embodiment of the present disclosure is schematically shown.

[0089] Furthermore, if Figure 5 As shown, step S230 includes, for example:

[0090] S231, corresponding to the person in charge, generates a swimming lane.

[0091] S232, the processing frame with only one association relationship and the backward association is arranged first.

[0092] S233, according to the timing of the actions of each processing frame, each processing frame is divided into a corresponding swimming lane.

[0093] S234, connecting each processing box in time sequence to obtain a process model.

[0094] According to an embodiment of the present disclosure, the persons in charge of all processing boxes are extracted, different lanes are drawn respectively, and the persons in charge are used as roles to define different lanes. The processing box includes, for example, an action box and a judgment box. The action box has at least one association relationship and at most two association relationships. The judgment box has at least three association relationships: one inflow and at least two outflows. When a processing box has only one association relationship, the processing box is the first or last processing box of the process model. The person in charge of the processing box that has only one association relationship and is backward-associated is used as the role of the first lane. According to the association relationship of the processing box / judgment box, the lanes are sorted in the order of appearance. For repeated roles, the first appearance shall prevail. According to the person in charge, the processing box that he is responsible for is placed in its lane. According to the association relationship of the processing box / judgment box, that is, the time sequence of the actions of each processing box, the connection lines of each processing box / judgment box are drawn to obtain the process model.

[0095] It can be understood that, according to the "interactive" verb, two directly related roles and their actions can be retrieved and mapped to the corresponding processing boxes, and the association relationship between the processing boxes / judgment boxes can be established.

[0096] Figure 6A diagram schematically shows a method for obtaining model labels according to an embodiment of the present disclosure.

[0097] Furthermore, the components also include attributives and adverbials. In order to describe the process model in more detail, relevant tags can be introduced to supplement the dimension information such as customers, products, partners and channels. Figure 6 As shown, the method for generating a business model also includes, for example:

[0098] S235, obtaining the customer type according to the attributive of the subject.

[0099] S236, obtain the product name according to the attributive of the object.

[0100] S237, obtaining a channel name and / or a partner name according to the adverbial clause.

[0101] S238, respectively matching the customer type, product name, channel name and partner name with the corresponding preset word library to obtain the corresponding model label.

[0102] S239, adding each model tag to the process model.

[0103] According to the embodiments of the present disclosure, for example, attributives and location adverbials are used to query existing customer, product, channel, and partner information, and the query results are placed in the corresponding information. Information that has not been queried is subsequently submitted to the architect to confirm whether to delete it. Attributives and adverbials are used to describe nouns in natural sentences. By extracting attributives and adverbials to form label words, after the process model is generated, the label words are annotated in the model label to facilitate subsequent comparison and matching with the corresponding information library.

[0104] Figure 7 A flow chart of a method for acquiring entity model elements according to an embodiment of the present disclosure is schematically shown.

[0105] Furthermore, if Figure 7 As shown, for example, the elements for constructing the entity model are acquired through steps S251 to S253.

[0106] S251, matching the valid nouns with the entity model library.

[0107] S252, in case of a match, the modified noun is taken as an attribute.

[0108] S253, in case of mismatch, the valid noun is taken as a newly added entity, and the modified noun is taken as an attribute of the newly added entity.

[0109] In this embodiment, a valid noun is, for example, a noun that has a noun word or sentence describing the noun. The noun in each sentence of the demand described in natural language, as well as the noun word and sentence describing the noun, are extracted using word segmentation and semantic analysis technology. Noun sentences describing the same noun are classified and stored as valid nouns. If a noun does not have a word or sentence describing it, the noun is deleted.

[0110] Specifically, the extracted nouns can be matched with existing nouns in the entity model library, and matched by synonyms. If the noun or its synonyms exist in the library, the noun describing the noun is used as an attribute and processed as a verb. If there is no noun or its synonyms 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 relevant description is used as the attribute description. The entity model library is supplemented by obtaining valid nouns, using the valid nouns as entity names, using the attributive nouns and adverbial nouns describing the valid nouns as attributes, and supplementing the enterprise standards as thresholds, so that the data of the constructed entity model library is guaranteed to be complete and correct.

[0111] It should be noted that if the acquired valid noun or its synonym already exists in the library, the corresponding word can be directly taken out from the library as a new valid noun.

[0112] In addition, if the corresponding attributes are less than the preset number, they will be supplemented by the architect later, or the valid name will be merged with similar words in the preset vocabulary. For example, when the non-key attributes of the newly added entity are less than 3, it will be transferred to the architect for analysis as an entity that cannot exist independently, and either the attributes will be supplemented or integrated into the existing entity.

[0113] Figure 8 A flow chart of a model quality inspection method according to an embodiment of the present disclosure is schematically shown.

[0114] According to an embodiment of the present disclosure, in order to improve the quality of model construction, before generating a business model based on a process model and an entity model, Figure 8 As shown, for example, steps S271 to S272 are used to check the correctness of the process model and the entity model respectively.

[0115] S271, analyzing the correctness of the process model according to the type of person in charge and the correlation between each processing box.

[0116] According to an embodiment of the present disclosure, a processing box includes, for example, an action box and a judgment box. An action box has at least one association relationship and at most two association relationships. A judgment box has at least three association relationships: one inflow and at least two outflows. At least one of the roles is non-system. The judgment box has at least two exits. There are no function boxes that are not associated with other function boxes. There are multiple contents in the customer, product, channel, and partner boxes in the same business model. According to this principle, the correctness of the association relationship and the completeness of the business requirements can be analyzed. Check whether all processing boxes are constructed correctly to avoid processing boxes that are not added to the process model, or processing boxes with incorrect sequences and labels.

[0117] S272, analyzing the correctness of the entity model based on the number of attributes.

[0118] According to the embodiments of the present disclosure, for example, when a newly added entity has less than 3 non-critical attributes, it is regarded as an entity that cannot exist independently and is transferred to the architect for analysis, and either attributes are supplemented or integrated into the existing entity. When a newly added or modified entity model has less than 5 attributes, it is considered not to meet the standard requirements.

[0119] Furthermore, in order to improve the real-time performance of the business model, the method for generating the business model may further include:

[0120] S280: When the specified conditions are met, update the business model to the business model library.

[0121] In this embodiment, by updating the preset word library as needed, the generation of business model flow charts in various situations can be adapted. There are many ways to update the preset word library, such as directly adding it by the architect. In this embodiment, different implementation methods are selected according to the different components of the preset word library. When the preset word library is a subject word library with components as subjects, the unmatched component words can be directly added to the preset word library. By updating each preset word library, the real-time update of the business model library can be achieved.

[0122] It should be noted that the prescribed conditions include, for example, after a certain period of operation, after a new business line is launched, or after a matching failure, etc., as long as the degree of matching of natural language can be guaranteed.

[0123] Fig. 9 The flowchart schematically shows the processing of the entire model building system according to an embodiment of the present disclosure.

[0124] like Fig. 9As shown, overall, after the user submits a requirement specification written in natural language, the business model construction system converts the natural language into a business model and performs a model compliance check. When the check is compliant, the business model is updated to the model library. When the check is not compliant, it is submitted to the architect for unified confirmation or adjustment before being updated to the model library.

[0125] Fig.10 The following schematically shows a logic diagram of converting a natural language into a model language according to an embodiment of the present disclosure.

[0126] like Fig.10 As shown in the figure, overall, the various components in a sentence can be obtained by analyzing a natural language sentence. Then, the subject, predicate, object, and conditional sentence conditions can be used to build a process model, and the subject's attributive, object's attributive, and location adverbial can be used to obtain customer tags, product tags, channel tags, and partner tags to supplement the process model, reflecting a complete process model construction logic.

[0127] Based on the above process model generation method, the present disclosure provides a specific embodiment:

[0128] Requirement description:

[0129] (Title) Individual customers can handle overseas study remittance business through their personal mobile banking.

[0130] (Requirement content) After individual customers click on the overseas study remittance in the mobile banking, the system pops up the overseas study remittance page for customers to fill in. After the customer selects the ID card and enters the number, the system verifies the ID card through the Ministry of Public Security system. When the ID card information elements do not match, the system prompts "The ID card you provided cannot be processed on the mobile banking for the time being, please go to the branch to process", and the customer clicks "Confirm" and the page returns to the remittance business page; when the ID card information elements match, the customer can enter the school name and other information.

[0131] (Subsequent processing omitted)

[0132] The first step is to extract basic component words and corresponding components from the demand content.

[0133] Subject: customer, system.

[0134] Both of them are preset word libraries, that is, standard role library contents, and do not need to be adjusted. If there is no synonym, for example, there is no customer in the role library, the extracted component words are directly used as component words. That is, customer is directly used as a component word.

[0135] Verbs: click, pop-up, select, enter, check, prompt, return, enter. These verbs are standard verbs and do not need to be adjusted.

[0136] Object: remittance for studying abroad, page, ID card, number.

[0137] Attributive: individual, remittance for studying abroad, networking.

[0138] In this embodiment, after matching with the current customer, product, and partner information database, the individual is filled in the customer tag of the process model, and the remittance for studying abroad is filled in the product tag of the process model. If the network does not exist in the relevant information database, the architect will confirm whether to delete it later.

[0139] Adverbial clause of place: personal mobile banking, Ministry of Public Security system.

[0140] After matching with the current customer, product, and partner information database, fill in the personal mobile banking into the channel label of the process model. The Ministry of Public Security system is not in the relevant information database, so submit it to the architect for confirmation.

[0141] Conditional sentence: When the certificate information elements do not match, the system prompts "The certificate you provided cannot be processed on the mobile banking, please go to the branch to process", and the customer clicks "Confirm" and the page returns to the remittance business page. If it matches, the customer can enter the school name and other information.

[0142] Action box combination. Combine the component words to get the action box "[]". (Customer) [click on remittance for studying abroad], (system) [pop-up page], (customer) [select ID card], (customer) [enter number], (system) online verification + ID card and number].

[0143] Get the judgment box. Extract the conditions in the conditional sentence to get the judgment box "<>", and combine the component words of the conditional sentence to get the action box "[]" directly related to the judgment box "<>". <Document elements match> (customer) [enter the school name], <Document elements do not match> (system) [prompt "The certificate you provided cannot be processed on mobile banking, please go to the branch to process"].

[0144] The second step is to establish the relationship within the model. Sort by the order of text description.

[0145] 1-(Customer)[Click on "Study Abroad Remittance"], 2-(System)[Pop-up page], 3-(Customer)[Select ID], 4-(Customer)[Enter number], 5-(System)[Verify + ID and number], 6-<Document elements are consistent>(Customer)[Enter school name], <Document elements are inconsistent>(System)[Prompt "The document you provided cannot be processed on mobile banking for the time being, please go to a branch to process"].

[0146] The third step is to draw the process model.

[0147] Draw swimlanes by role and get two swimlanes: one for customer and one for system.

[0148] Assign customers and systems to roles in the lanes.

[0149] Put the action boxes whose results are customers obtained in the second step in order into the swimlane whose role is customers, and put the action boxes and judgment boxes whose results are systems in order into the swimlane whose role is systems.

[0150] Draw lines between the action boxes in the order obtained from the second step. There are two "<>"s with opposite meanings. For example, if you want to connect the negative meaning, you can leave <Document elements do not match>, followed by the corresponding action box. For example, you can express the positive and negative words on the line connecting "<>" and "[]".

[0151] Fill in the customer, product, channel, and partner labels determined in the second step into the process model labels.

[0152] After the flowchart is completed, it is submitted to the architect for review. According to the label, it is prompted "Internet, Ministry of Public Security system, non-customer, product, channel, partner, please confirm whether to adjust the model." The architect will perform relevant processing in the system.

[0153] Specifically, the present disclosure provides a specific embodiment of updating a physical model library.

[0154] There is a paragraph in the business requirements specification that says "Electronic ID card has been added to the document type. Users can handle related business with the electronic ID card."

[0155] The first step is to analyze nouns. Using word segmentation and semantic analysis techniques, we can find that the initial noun of the first sentence is "identity document". The initial nouns of the second sentence are user, identity card, and business. For example, if there are no sentences describing users and businesses in the entire paragraph, users and businesses are deleted. If there are two sentences describing the noun of identity card, then the noun is a valid noun.

[0156] The second step is to search the existing database with valid nouns. If the noun exists in the existing entity, the attributive of the noun is picked out and a synonym match is performed in the entity. The attributive of the ID card is "electronic". If it is not found in the existing entity, "electronic" is added as an attribute to the existing entity.

[0157] The third step is to supplement the domain value. Use the domain value of the enterprise-level information standard to supplement the domain value corresponding to the electronic ID card attribute. If there is no relevant domain value in the enterprise-level information standard, it will be left blank and submitted to the architect for supplementation.

[0158] In summary, the present disclosure provides a method for generating a business model. Through multi-dimensional analysis of natural language, natural language is converted into a corresponding business model to meet the needs of reflecting the business from different dimensions, ensuring that the business model is updated synchronously with business needs, and reducing the large amount of manpower investment and time occupied in building and maintaining the business model.

[0159] Based on the above business model generation method, the present disclosure provides a business model generation device. Fig.11 The device is described in detail.

[0160] Fig.11 The structural block diagram of a device for generating a business model according to an embodiment of the present disclosure is schematically shown.

[0161] like Fig.11 As shown, another aspect of the present disclosure provides a device 1100 for generating a business model, for example, including: an acquisition module 1110 , a process module 1120 , an entity module 1130 and a generation module 1140 .

[0162] The acquisition module 1110 is used to acquire all the component words and corresponding components of the natural sentence in the demand content. In one embodiment, the acquisition module 1110 can be used to perform the operation S210 described above, which will not be repeated here.

[0163] The process module 1120 is used to match the component words with the corresponding preset word library according to the components, obtain multiple processing boxes, and arrange and associate the multiple processing boxes according to the action occurrence sequence and the number of association relationships of each processing box to obtain a process model. In one embodiment, the process module 1120 can be used to perform the operations S220 to S230 described above, which will not be repeated here.

[0164] The entity module 1130 is used to obtain the effective nouns and modified nouns in the component words, wherein the modified nouns describe the effective nouns, perform a database search on the effective nouns, use the effective nouns as entities, use the modified nouns as attributes, query the enterprise-level information standards, obtain the thresholds corresponding to the attributes, and obtain the entity model according to the entities, attributes and thresholds. In one embodiment, the entity module 1130 can be used to perform the operations S240 to S260 described above, which will not be repeated here.

[0165] The generation module 1140 is used to generate a business model according to the process model and the entity model. In one embodiment, the generation module 1140 can be used to perform the operation S270 described above, which will not be described in detail here.

[0166] According to an embodiment of the present disclosure, any multiple modules among the acquisition module 1110, the process module 1120, the entity module 1130 and the generation module 1140 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 1110, the process module 1120, the entity module 1130 and the generation module 1140 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of the acquisition module 1110 , the process module 1120 , the entity module 1130 and the generation module 1140 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.

[0167] Fig.12 A block diagram of an electronic device suitable for implementing a method for generating a business model according to an embodiment of the present disclosure is schematically shown.

[0168] like Fig.12 As shown, the electronic device 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 part 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 a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1201 may also include an onboard memory for caching purposes. The processor 1201 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0169] In RAM 1203, various programs and data required for the operation of electronic device 1200 are stored. Processor 1201, ROM 1202 and RAM 1203 are connected to each other via bus 1204. Processor 1201 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 1202 and / or RAM 1203. It should be noted that the program can also be stored in one or more memories other than ROM 1202 and RAM 1203. Processor 1201 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in one or more memories.

[0170] According to an embodiment of the present disclosure, the electronic device 1200 may further include an input / output (I / O) interface 1205, which is also connected to the bus 1204. The electronic device 1200 may further include one or more of the following components connected to the I / O interface 1205: an input portion 1206 including a keyboard, a mouse, etc.; an output portion 1207 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1208 including a hard disk, etc.; and a communication portion 1209 including a network interface card such as a LAN card, a modem, etc. The communication portion 1209 performs communication processing via a network such as the Internet. A drive 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, etc., is installed on the drive 1210 as needed, so that a computer program read therefrom is installed into the storage portion 1208 as needed.

[0171] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0172] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus, or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 1202 and / or RAM 1203 described above and / or one or more memories other than ROM 1202 and RAM 1203.

[0173] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method for generating a business model provided by the embodiment of the present disclosure.

[0174] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 1201. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0175] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1209, and / or installed from the removable medium 1211. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0176] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1209, and / or installed from the removable medium 1211. When the computer program is executed by the processor 1201, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0177] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0178] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0179] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0180] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

[0181] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present disclosure. It should be understood that the above description is only a specific embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for generating a business model, characterized in that: include: Obtain all the component words and corresponding components of the natural sentences in the required content; According to the components, the component words are matched with corresponding preset word libraries to obtain multiple processing boxes; Arrange and associate the plurality of processing blocks according to the action occurrence sequence and the number of association relationships of each processing block 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; querying the 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; Generate a business model according to the process model and the entity model; The components include a subject, a predicate and an object. According to the components, the component words are matched with corresponding preset word libraries to obtain multiple processing boxes including: Taking the subject in each of the natural sentences as a role, obtaining the person in charge of each of the processing boxes; Respectively obtain synonyms of each of the predicates and each of the objects in a corresponding preset vocabulary; Placing each of the synonyms after each of the responsible persons to obtain a plurality of the processing boxes; The process model is obtained by arranging and associating the plurality of processing blocks according to the action occurrence sequence and the number of association relationships of the processing blocks, and comprising: Generate a swimming lane corresponding to the person in charge; There is only one association relationship, and the processing box that is associated backward is arranged first; According to the timing of the actions of each processing frame, each processing frame is arranged in a corresponding swimming lane; The process model is obtained by connecting the processing blocks according to the time sequence.

2. The method for generating a business model according to claim 1, characterized in that: The natural sentence includes a non-conditional sentence and a conditional sentence. According to the components, the component words are matched with corresponding preset vocabulary to obtain multiple processing boxes including: According to the components of the non-conditional sentence, an action frame is obtained; According to the components of the conditional sentence, a judgment box is obtained.

3. The method for generating a business model according to claim 1, characterized in that: The components also include attributives and adverbials, and the method for generating the business model also includes: According to the attributive of the subject, the customer type is obtained; According to the attributive of the object, obtain the product name; Obtaining the channel name and / or partner name according to the adverbial clause; Respectively matching the customer type, the product name, the channel name, and the partner name with corresponding preset word libraries to obtain corresponding model labels; Each of the model tags is added to the process model.

4. The method for generating a business model according to claim 1, characterized in that: The taking the effective noun as an entity and the modified noun as an attribute includes: Matching the valid nouns with the entity model library; In case of a match, the modified noun is taken as an attribute; In case of mismatch, the effective noun is taken as a newly added entity, and the modified noun is taken as an attribute of the newly added entity.

5. The method for generating a business model according to claim 1, characterized in that: The acquisition of all the constituent words and corresponding components of the natural sentence in the required content includes: Performing word segmentation and decomposition on the natural sentence line by line to obtain the component words; According to semantic analysis, the components of each of the component words are obtained.

6. The method for generating a business model according to claim 1, characterized in that: Before generating a business model according to the process model and the entity model, the method for generating the business model further includes: Analyze the correctness of the process model according to the type of the person in charge and the correlation between the processing boxes; Based on the number of attributes, the correctness of the entity model is analyzed.

7. The method for generating a business model according to claim 6, characterized in that: After generating the business model, the method for generating the business model further includes: The business model is updated to the business model library.

8. A device for generating a business model, characterized in that: include: An acquisition module is used to acquire all the component words and corresponding components of the natural sentences in the required content; A process module, for matching the component words with corresponding preset word libraries according to the components to obtain a plurality of processing boxes; Arrange and associate the plurality of processing blocks according to the action occurrence sequence and the number of association relationships of each processing block to obtain a process model; An entity module is used to obtain effective nouns and modified nouns in the component words, wherein the modified nouns describe the effective nouns; take the effective nouns as entities and the modified nouns as attributes; query enterprise-level information standards to obtain thresholds corresponding to the attributes; obtain an entity model based on the entity, the attributes and the thresholds; and, A generating module, used for generating a business model according to the process model and the entity model; The components include a subject, a predicate and an object. According to the components, the component words are matched with corresponding preset word libraries to obtain multiple processing boxes including: Taking the subject in each of the natural sentences as a role, obtaining the person in charge of each of the processing boxes; Respectively obtain synonyms of each of the predicates and each of the objects in a corresponding preset vocabulary; Placing each of the synonyms after each of the responsible persons to obtain a plurality of the processing boxes; The process model is obtained by arranging and associating the plurality of processing blocks according to the action occurrence sequence and the number of association relationships of the processing blocks, and comprising: Generate a swimming lane corresponding to the person in charge; There is only one association relationship, and the processing box that is associated backward is arranged first; According to the timing of the actions of each processing frame, each processing frame is arranged in a corresponding swimming lane; The process model is obtained by connecting the processing blocks according to the time sequence.

9. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the method for generating a business model according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, and when the instructions are executed by a processor, the processor executes the method for generating a business model according to any one of claims 1 to 7.

11. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method for generating a business model according to any one of claims 1 to 7.

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