Method, apparatus, and electronic device for determining associated assets

Abstract

The present disclosure provides a method, apparatus, and electronic device for determining associated assets, which are applied to the fields of artificial intelligence or finance, etc. The method includes: obtaining input information from a client; in response to the input information, performing a match in a keyword library based on the input information to determine directly associated assets corresponding to the input information, where the keyword library includes the corresponding relationships between assets and keywords in the business architecture; and determining derivative associated assets for the directly associated assets based on the association relationships between multiple assets in the business architecture.

Description

Technical Field

[0001] The present disclosure relates to the fields of artificial intelligence and financial technology, and more particularly, to a method, apparatus, and electronic device for determining associated assets. Background Art

[0002] To improve the adaptability between projects and institutional business, institutional organization, and institutional strategy, project work can be guided based on the business architecture. Many enterprises can build a good business architecture, but it is difficult to continuously manage and control the business architecture.

[0003] In the process of implementing the concept of the present disclosure, the applicant found that there are at least the following problems in the related art. The business architecture model has a high degree of abstraction and complex association relationships, making it difficult to apply the business architecture to daily R & D work, and it is not convenient for business R & D personnel to clearly determine the content of the business architecture corresponding to the project, and it is difficult to continuously maintain the business architecture, resulting in the business architecture being difficult to continuously play its role. Summary of the Invention

[0004] In view of this, the present disclosure provides a method, apparatus, and electronic device for determining associated assets to reduce the difficulty of assets for continuously maintaining the business architecture.

[0005] One aspect of the present disclosure provides an associated asset determination method executed by a server side, including: obtaining input information from a client; in response to the input information, performing a match in a keyword library based on the input information to determine a directly associated asset corresponding to the input information, where the keyword library includes a corresponding relationship between assets and keywords in the business architecture; and determining a derivative associated asset for the directly associated asset based on the association relationship between multiple assets in the business architecture.

[0006] According to an embodiment of the present disclosure, the input information includes a chapter name; performing a match in the keyword library based on the input information to determine a directly associated asset corresponding to the input information includes: extracting the chapter name in the input information; and using the chapter name to perform a match in the keyword library to determine a directly associated asset corresponding to the chapter name.

[0007] According to an embodiment of the present disclosure, the chapter name includes a phrase composed of a verb and a noun; using the chapter name to perform a match in the keyword library to determine a directly associated asset corresponding to the chapter name includes: segmenting the chapter name to obtain a segmentation result; taking the whole of adjacent verbs and nouns in the segmentation result as a keyword group; and using the keyword group to perform a match in the keyword library to obtain a directly associated asset corresponding to the keyword group.

[0008] According to an embodiment of the present disclosure, the input information includes chapter content; based on the input information, matching is performed in a keyword library to determine the directly associated assets corresponding to the input information, including: obtaining summary information of the chapter content; extracting keywords from the summary information; and using the keywords to perform matching in the keyword library to obtain the directly associated assets corresponding to the keywords.

[0009] According to an embodiment of the present disclosure, extracting keywords from the summary information includes: extracting keywords from the summary information based on at least one of word frequency, word importance, page ranking, or word vectors.

[0010] According to an embodiment of the present disclosure, keywords have levels, and the level of a keyword is determined based on the accuracy of the associated assets of the keyword; the above method further includes: when multiple keywords are obtained, screening associated assets from the directly associated assets corresponding to the multiple keywords based on the levels of the keywords.

[0011] According to an embodiment of the present disclosure, the indicators of accuracy include at least one of the frequency of user use and the adoption rate; the above method further includes: obtaining historical user data through buried point records; determining at least one of the frequency of user use and the adoption rate from the historical user data; and updating the keyword library based on at least one of the frequency of user use and the adoption rate.

[0012] According to an embodiment of the present disclosure, the associated assets of low-level keywords are enclosed within the scope of the associated assets of high-level keywords.

[0013] According to an embodiment of the present disclosure, the above method further includes: if the chapter name includes a specified field, then discarding the chapter content corresponding to the chapter name.

[0014] According to an embodiment of the present disclosure, the construction method of the keyword library includes at least one of the following: storing the keywords from the client and the assets of the business architecture associatively in a specified storage space; or expanding the corresponding relationships in the keyword library based on the corresponding relationships between the existing assets and the keywords and the corresponding relationships between the assets.

[0015] According to an embodiment of the present disclosure, the above method further includes: sending the associated assets corresponding to the input information to the client; receiving update information from the client regarding the associated assets corresponding to the input information; and if the login account of the client has the keyword library maintenance permission, then updating the keyword library based on the update information.

[0016] According to an embodiment of the present disclosure, determining the derivative associated assets for the directly associated assets based on the association relationships between multiple assets in the business architecture includes: if the directly associated assets include a common task, then expanding the directly associated assets a specified number of times based on the association relationships between the assets in the business architecture to obtain the derivative associated assets of the directly associated assets.

[0017] One aspect of the present disclosure provides an associated asset determination device, including: an input information acquisition module configured to acquire input information from a client; a direct associated asset determination module configured to, in response to the input information, perform matching in a keyword library based on the input information to determine a direct associated asset corresponding to the input information, where the keyword library includes a correspondence between assets in a business architecture and keywords; and a derivative associated asset determination module configured to determine derivative associated assets for the direct associated asset based on an association relationship between multiple assets in the business architecture.

[0018] Another aspect of the present disclosure provides an electronic device, including one or more processors and a storage device, where the storage device is configured to store executable instructions, and when the executable instructions are executed by the processor, the above method is implemented.

[0019] Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, and the instructions are used to implement the above method when executed.

[0020] Another aspect of the present disclosure provides a computer program, where the computer program includes computer-executable instructions, and the instructions are used to implement the above method when executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, the above and other objects, features, and advantages of the present disclosure will become clearer. In the drawings:

[0022] Figure 1 Schematically shows an exemplary system architecture to which the associated asset determination method, device, and electronic device according to the embodiments of the present disclosure can be applied;

[0023] Figure 2 Schematically shows a flowchart of the associated asset determination method according to the embodiments of the present disclosure;

[0024] Figure 3 Schematically shows a schematic diagram of a business architecture according to the embodiments of the present disclosure;

[0025] Figure 4 Schematically shows a flowchart of determining a direct associated asset corresponding to a chapter name according to the embodiments of the present disclosure;

[0026] Figure 5 Schematically shows a flowchart of determining a direct associated asset corresponding to input information according to the embodiments of the present disclosure;

[0027] Figure 6 Schematically shows a schematic diagram of providing business services according to the embodiments of the present disclosure;

[0028] Figure 7 A schematic diagram showing the corresponding relationship between assets in the service architecture and assets in the Internet architecture according to an embodiment of the present disclosure;

[0029] Figure 8 A block diagram schematically showing an associated asset determination device according to an embodiment of the present disclosure;

[0030] Figure 9 A schematic diagram showing the process of analyzing architectural assets according to an embodiment of the present disclosure; and

[0031] Figure 10 A block diagram schematically showing an electronic device according to an embodiment of the present disclosure. Detailed implementation manners

[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 merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, many specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments can be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts of the present disclosure.

[0033] The terms used herein are merely for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising" and the like as used herein indicate the presence of features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0034] All terms used herein (including technical and scientific terms) 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] In the case of using expressions such as "at least one of A, B, and C", generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but not be limited to a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C). The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features.

[0036] To help business R & D personnel quickly map the business content in a project to the corresponding business models in the business architecture, it is necessary to encapsulate the corresponding relationships between assets (such as models) in the business architecture, so as to determine the logical path from some keywords in the project-related documents to the relevant business models. This can achieve the automatic output of the business architecture asset scope associated with the project, effectively reducing the difficulty for R & D personnel or architecture management personnel to map and analyze the business architecture.

[0037] Embodiments of the present disclosure provide a method, apparatus, and electronic device for determining associated assets. The method for determining associated assets includes a direct associated asset determination process and a derivative associated asset determination process. In the direct associated asset determination process, first, input information from a client is obtained. Then, in response to the input information, a match is made in a keyword library based on the input information to determine the direct associated assets corresponding to the input information. The keyword library includes the corresponding relationships between assets and keywords in the business architecture. After completing the direct associated asset determination process, the derivative associated asset determination process is entered, and the derivative associated assets for the direct associated assets are determined based on the association relationships between multiple assets in the business architecture.

[0038] The method, apparatus, and electronic device for determining associated assets provided by the embodiments of the present disclosure automatically analyze the business architecture assets involved in the text information based on the constructed keyword library for standardized and normalized text information, effectively reducing the high requirements for analysts' mastery and application abilities of business architecture knowledge, so that users can intelligently delimit the asset scope of the business architecture involved in the text information after submitting standardized and normalized text information (such as a business requirements document).

[0039] The method, apparatus, and electronic device for determining associated assets provided by the embodiments of the present disclosure can be used in the field of artificial intelligence for aspects related to determining associated assets, and can also be used in various fields other than the field of artificial intelligence, such as the financial field. The application fields of the method, apparatus, and electronic device for determining associated assets provided by the embodiments of the present disclosure are not limited.

[0040] Figure 1 Schematically shows an exemplary system architecture to which the method, apparatus, and electronic device for determining associated assets according to the embodiments of the present disclosure can be applied. It should be noted that Figure 1 What is shown is only an example of the system architecture to which the embodiments of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments, or scenarios.

[0041] Such as Figure 1As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and servers 105, 106, 107. The network 104 may include multiple gateways, routers, hubs, network cables, etc., which are used to provide a medium for communication links between the terminal devices 101, 102, 103 and the servers 105, 106, 107. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0042] Users can use the terminal devices 101, 102, 103 to interact with other terminal devices and servers 105, 106, 107 through the network 104 to receive or send information, such as receiving associated asset requests, sending processing results, etc. The terminal devices 101, 102, 103 may be installed with various communication client applications. For example, operation and maintenance applications, asset management applications, software development applications, banking applications, government affairs applications, monitoring applications, web browser applications, search applications, office applications, instant messaging tools, email clients, social platform software, etc. (only for examples). For example, users can use the terminal device 101 to view the impact scope of a project. For example, users can use the terminal device 102 to query business architecture assets. For example, users can use the terminal 103 to view associated assets and perform software development, etc. based on the requirements of the associated assets.

[0043] The terminal devices 101, 102, 103 include but are not limited to smartphones, virtual reality devices, augmented reality devices, tablets, laptop computers, desktop computers, etc.

[0044] The servers 105, 106, 107 can receive requests and process the requests. Specifically, they can be storage servers, background management servers, server clusters, etc. For example, the server 105 may store a business architecture description model. The server 106 can be used to determine keywords, such as determining keywords through word frequency, semantics, etc. The server 107 can be a server for storing a keyword library and a keyword library. The background management server can analyze and process the received asset location requests, architecture asset maintenance requests, etc., and feedback the processing results (such as the requested assets, processing results, etc.) to the terminal devices.

[0045] It should be noted that the method for determining associated assets provided by the embodiments of the present disclosure can generally be executed by servers 105, 106, and 107. Correspondingly, the apparatus for determining associated assets provided by the embodiments of the present disclosure can generally be disposed in servers 105, 106, and 107. The method for determining associated assets provided by the embodiments of the present disclosure can also be executed by a server or a server cluster different from servers 105, 106, and 107 and capable of communicating with terminal devices 101, 102, 103 and / or servers 105, 106, and 107.

[0046] It should be understood that the numbers of terminal devices, networks, and servers are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers.

[0047] Figure 2 A flowchart of the method for determining associated assets according to an embodiment of the present disclosure is schematically shown. This method for determining associated assets is executed by the server side.

[0048] As Figure 2 shown, this method for determining associated assets may include operation S210 to operation S230.

[0049] In operation S210, input information from the client is obtained.

[0050] In this embodiment, the input information of the client can be text information. For example, the input information is search information input by the user (such as document name, document identifier, keyword, etc.), document information input by the user, etc. In addition, the input information of the client can be voice information. For example, the search instruction input by the user in a voice manner, the document information read aloud, etc. In addition, the input information of the client can be image information. For example, the image information input by the user, and the image information may include images corresponding to the search instruction and the document information.

[0051] In operation S220, in response to the input information, a match is made in the keyword library based on the input information to determine the directly associated assets corresponding to the input information. The keyword library includes the corresponding relationship between the assets in the business architecture and the keywords.

[0052] Among them, the keyword library can be constructed in advance based on expert experience, etc. For example, the keyword library can be formed by experts inputting relevant keywords and architecture assets and storing them associatively. For example, the keyword library can be selected from a large number of assets in the business architecture based on symbiotic relationships, etc. For example, the keyword library can first select basic keywords and basic associated assets corresponding to the basic keywords based on expert experience and symbiotic relationships, and then determine other associated assets related to the basic associated assets based on the association relationship between the basic associated assets and each asset in the business architecture, so as to expand the keyword library based on the other associated assets.

[0053] Specifically, multiple words or terms can be obtained from the input information through methods such as word segmentation, and then by matching in the keyword library, the keywords that match successfully can be obtained, so as to obtain the directly associated assets corresponding to the keywords that match successfully.

[0054] In operation S230, based on the association relationships between multiple assets in the business architecture, the derivative associated assets for the directly associated assets are determined.

[0055] Figure 3 A schematic diagram of a business architecture according to an embodiment of the present disclosure is schematically shown.

[0056] Such as Figure 3 As shown, the business architecture may include at least one of a product model, a process model, an entity model, and a market model. There is a first sub-corresponding relationship between the assets of the product model and the assets of the process model, and a second sub-corresponding relationship between the assets of the process model and the assets of the entity model.

[0057] Among them, regarding the process model, a process model may include multiple business domains, a business domain may involve multiple value streams, a value stream may include multiple activities, and an activity may be composed of multiple tasks.

[0058] Regarding the product model, a product model may have multiple product lines, a product line may include multiple product groups, a product group may have multiple basic products, and a basic product may have multiple salable products.

[0059] Regarding the entity model, an entity model may include multiple business objects, and a business object may have entities such as a core entity, a life cycle, a subordinate entity, a business object relationship, and constraint conditions.

[0060] Regarding the market model, a market model may include multiple customers, a customer may correspond to multiple channels, and a channel may have multiple partners.

[0061] Specifically, the business architecture may be constructed based on value streams. Among them, the process model is for multiple dimensions, such as it can be divided into an activity value chain, a task process, and business components.

[0062] The entities involved in the process model can be characterized by the entity model. The data of the entity model can be stored in a database. In actual use, the entity model can be refined and dataized to obtain a data structure model, which can be operated by a business object service.

[0063] Based on the above relationships, it is possible to determine derivative associated assets for directly associated assets. For example, if a keyword in the input information hits a certain task, then the activities associated with the task can be determined based on the association relationship between the activity and the task. In addition, entities involved in the task can also be determined, etc., to obtain derivative associated assets.

[0064] In the above manner, the business architecture assets associated with the input information can be determined.

[0065] It should be noted that some enterprises have structured requirements for documents, etc. For example, the document needs to have chapters, and the chapter names of the chapters need to include key information, etc. For such structured input information, keywords can be quickly determined from the input information through this structured feature, so as to quickly determine the associated assets corresponding to the input information.

[0066] In some embodiments, the input information includes chapter names. Correspondingly, based on the input information, matching in the keyword library, determining the directly associated assets corresponding to the input information may include the following operations. First, extract the chapter names in the input information. Then, use the chapter names to perform matching in the keyword library to determine the directly associated assets corresponding to the chapter names. For example, first, extract the chapter names from the input text information based on the structured features, and then perform word segmentation processing on the chapter names, etc., to obtain the word segmentation results, which may include multiple words or phrases, etc. After removing words that cannot be keywords such as modal particles, perform matching in the keyword library to obtain the associated assets.

[0067] In some embodiments, the chapter names may also have structured information. For example, the chapter names may include phrases composed of verbs and names, which can clearly express semantic information through the phrases and are also convenient for locating associated assets in the business architecture.

[0068] Figure 4 Schematically shows a flowchart of determining directly associated assets corresponding to chapter names according to an embodiment of the present disclosure. Among them, the chapter names may include phrases composed of verbs and nouns.

[0069] As Figure 4 shown, using the chapter names to perform matching in the keyword library, determining the directly associated assets corresponding to the chapter names may include operation S401 to operation S403.

[0070] In operation S401, perform word segmentation on the chapter names to obtain the word segmentation results.

[0071] Specifically, the chapter names can be segmented through a dictionary, a Chinese dictionary, a special field dictionary (such as a dictionary related to the architecture field), etc., to obtain multiple characters, words, etc.

[0072] In operation S402, the whole of adjacent verbs and nouns in the word segmentation result is used as a keyword group. Specifically, the parts of speech of multiple words obtained by word segmentation can be determined, such as including nouns, verbs, adjectives, numerals, quantifiers, pronouns, etc. The words whose parts of speech are nouns and verbs are determined therefrom. If certain verbs and nouns are adjacent in the chapter name, the whole of the verb and the noun can be used as a keyword. Compared with the keywords determined only by using co-occurrence relationships or word frequencies, etc., the keywords composed of verbs and nouns have richer semantics and less noise, can effectively improve the accuracy of the determined associated assets, and reduce the computing resources consumed for determining keywords and shorten the response time.

[0073] In operation S403, the keyword group is used for matching in the keyword library to obtain the directly associated assets corresponding to the keyword group. For example, there is a corresponding relationship between the keyword group and the assets of the business architecture in the keyword library.

[0074] For example, according to the chapter content of the standardized business requirements document, keyword retrieval can be carried out specifically by using word segmentation technology and semantic analysis technology. According to the form of verb + noun, the directly associated assets of the business architecture are retrieved.

[0075] In some embodiments, the embodiments of the present disclosure can equally be applicable to input information that does not have a structured feature or the structured feature does not include a chapter name. For example, at least part of the noise information is excluded from a large amount of input information based on semantic information, etc., and then keyword extraction is carried out.

[0076] Figure 5 A flowchart showing the determination of directly associated assets corresponding to the input information according to the embodiments of the present disclosure is schematically shown. For example, the input information includes chapter content.

[0077] As Figure 5 shown, based on matching in the keyword library with the input information, determining the directly associated assets corresponding to the input information may include operations S501 to S503.

[0078] In operation S501, summary information of the chapter content is obtained. Among them, summary information can be extracted from the chapter content based on semantic information, such as extracting words with high word frequencies and semantics similar to the semantics of the chapter content from the chapter content, and automatically generating summary information through these extracted words.

[0079] In operation S502, keywords are extracted from the summary information. For example, a keyword group is extracted from the summary information, and the keyword group includes a verb and a noun.

[0080] In operation S503, the keywords are used for matching in the keyword library to obtain the directly associated assets corresponding to the keywords.

[0081] In some embodiments, extracting keywords from abstract information includes: extracting keywords from abstract information based on at least one of word frequency, word importance, page ranking, or word vectors.

[0082] In some embodiments, the extraction process of abstract information and keywords can be shown as follows.

[0083] For example, using the TF-IDF algorithm, based on word frequency, an "importance" weight is assigned to each word. By analyzing and training a large amount of large texts, the idf (weight) value is trained to generate business keywords under a large corpus. If some words appear frequently in a certain article and rarely in other articles, then these words may better reflect the main idea of this article. Among them, the IF-IDF value: TF * IDF, and the value is proportional to the importance of the word to the article. TF = the number of times the word appears in the article / the total number of words in the article or the number of times the word appears in the article / the number of times the most frequently appearing word in the article appears. IDF = log (the total number of documents in the corpus / (the number of documents containing the word + 1)).

[0084] It should be noted that this algorithm requires large text training and is more suitable for keyword production under a large corpus. The result of keyword extraction for a single document is slightly worse. Therefore, historical input data can be used for large text training to determine the keyword library.

[0085] Since some words appear frequently in a certain document but rarely in the remaining documents, we consider these words to be more able to elaborate on the main idea of this document. This algorithm excludes some common words such as "de", "di", "de".

[0086] Taking the TextRank algorithm as an example. If a web page is linked to by many other web pages, it means that this web page is very important (the PageRank value will be relatively high). If a web page with a high PageRank value links to another web page, then the PageRank value of the linked web page will also increase accordingly.

[0087] The PageRank algorithm assigns a PR (PageRank) value to each web page in advance. Since the PR value is the probability that a web page is accessed, it is generally 1 / N, where N is the total number of web pages, and its value can be a positive integer greater than or equal to 1. During a certain period of time, through the link relationship between web pages, the PR value of each web page is calculated, and then the importance of the web pages is ranked according to the PR value.

[0088] TextRank divides the text into complete sentences, performs word segmentation on each sentence, removes some meaningless words such as "的、得、地", and the words are considered candidate keywords. Candidate keywords are considered as each node. Usually, there are at most N (a positive integer greater than 1) candidate keywords in a sentence, and then the co-occurrence relationship (words that appear in a sentence at the same time are related) is used to construct the edge between any two points.

[0089] Compared with the PageRank algorithm, the TextRank algorithm has an additional weight term to indicate the different degrees of importance of the edge connection between two nodes. Therefore, the TextRank algorithm is essentially a modified PageRank algorithm for keyword extraction and automatic summary extraction. It does not require large text training and is more suitable for keyword extraction under a single document. Therefore, the TextRank algorithm can be used when extracting keywords from a single document. For example, after the user sends the project establishment document of a new project that needs to determine the associated assets to the server through the client, the server can extract keywords from the project establishment document through the TextRank algorithm to determine the associated assets.

[0090] For example, the TextRank algorithm is used to segment text sentences, and the co-occurrence relationship is used to build edge connections between nodes to extract business keywords from a single document.

[0091] Take word vectorization (Word2Vec) to extract keywords as an example.

[0092] Word2Vec is a language model that learns semantic knowledge from a large amount of text corpus in an unsupervised manner and is widely used in natural language processing. The characteristic of Word2Vec is that all words are vectorized, so that the relationship between words can be quantitatively measured, allowing the machine to explore the connection between words at the semantic level.

[0093] The training models of Word2Vec include CBOW (Continuous Bag-of-Words Model) and Skip-gram (Continuous Skip-gram Model) based on input and output. CBOW takes the words contained in the context of a word as input and the word itself as output. Skip-gram is just the opposite. Word2Vec is essentially a dimensionality reduction operation, which represents each word in natural language as a short vector with unified meaning and unified dimension, which lays the foundation for machine calculation and processing of short vectors. Word vectorization requires large text training and is more suitable for use in the process of building a keyword library.

[0094] Currently, the extraction of keywords uses unsupervised machine learning methods. Based on the machine learning of a large number of requirement documents, new keyword results are obtained using TextRank and Word2Vec. TextRank is based on statistics, and Word2Vec is based on semantics.

[0095] For example, Example: 127 0.013591352708262904 For applications with pre-query requirements, customers who hold our bank cards and have their mobile phone numbers reserved are supported to submit credit investigation authorizations through WeChat, etc. *** Introduce **** technology, and complete customer identity confirmation in the way of ** mobile phone number + ****, cancel the signing of *****, and the ** investigation officer queries the ** record through the ***** system and then conducts the ** query.

[0096] Among them, 127 refers to the 127th sentence in the article, and 0.013591352708262904 is the weight value of the sentence calculated by the TextRank algorithm. This algorithm first uses word segmentation technology to classify the words in the sentence according to their parts of speech or filter some words such as "de", "di", "de". Then, it is considered that the words that appear in the same sentence are related. Each word (at most 3) in a sentence is regarded as a node, and there is a relationship between the nodes. It forms an undirected weighted edge, and the weight value of the edge is the number of times these nodes and edges appear. Then, according to algorithms such as PageRank, the weight value is supplemented, and then according to the size of the weight value, the keywords are typed out. In fact, the keyword is the same concept, but several adjacent keywords are combined to form a keyword group. And the abstract is to regard each sentence as a node. The relevance is determined by the size of the weight value.

[0097] As shown in the above abstract information, it may include keyword groups: submit (verb) credit investigation authorization (noun), customer identity (noun) confirmation (verb), query ** record, sign *****, introduce **** technology, conduct ** query, etc. These keyword groups can facilitate determining which assets in the chapter content are associated with the business architecture.

[0098] In some embodiments, the directly associated assets obtained can also be screened based on the level of the keywords, improving the hit rate of the output associated assets.

[0099] For example, the keyword has a level, and the level of the keyword is determined based on the accuracy of the associated assets of the keyword.

[0100] Correspondingly, the above method may further include the following operations: when multiple keywords are obtained, screen the associated assets from the directly associated assets corresponding to the multiple keywords based on the level of the keywords.

[0101] Among them, the indicators of accuracy may include at least one of the frequency of user use and the adoption rate. This accuracy can reflect the accuracy of the associated assets output by the system. For example, it can be determined by experts whether the associated assets are correct.

[0102] Specifically, the above method may further include the following operations.

[0103] First, obtain historical user data through buried point records.

[0104] Then, determine at least one of the frequency of user use and the adoption rate from the historical user data.

[0105] Next, update the keyword library based on at least one of the frequency of user use and the adoption rate.

[0106] Specifically, according to the opinions of passing, not passing, or modifying issued by expert review, automatically mark the keywords, and through unsupervised machine learning methods, iteratively improve the keyword retrieval program. In addition, perform machine learning on the buried point records during the user's use process, and optimize the keyword level by using the voting machine learning method based on the frequency of user use and the adoption rate.

[0107] In some embodiments, the associated assets of low-level keywords are enclosed within the scope of the associated assets of high-level keywords.

[0108] For example, through keyword grading, the architecture assets associated with low-level keywords should be enclosed within the scope of the architecture assets associated with high-level keywords. For example: Keyword A is associated with field a, and if keyword B is associated with activity c in field a and activity d in field b, the level of keyword A is higher than that of keyword B. At this time, the assets in field b in the associated assets of keyword B should be trimmed to avoid conflicts with the associated assets of keyword A. This further narrows the scope of the output associated assets and helps to improve the reference value of the output results.

[0109] In some embodiments, in order to reduce the content to be retrieved, the above method may further include the following operation: if the chapter name includes a specified field, discard the chapter content corresponding to the chapter name.

[0110] For example, if fields such as "query", "login", and "technical transformation" exist in the title of the "main content of requirements" chapter of the requirements document, the requirements of this small chapter are set to "not involving business architecture adjustment".

[0111] In some embodiments, the construction method of the keyword library includes at least one of the following: storing the keywords from the client and the assets of the business architecture in an associated manner in a specified storage space; or expanding the corresponding relationships in the keyword library based on the corresponding relationships between the existing assets and the keywords and the corresponding relationships between the assets.

[0112] For example, an association relationship between business keywords and directly associated business architecture assets can be established, and based on the relationship between keywords and business architecture assets, as well as the association relationship between business architecture assets, the relationship between business keywords and all business architecture assets can be automatically completed.

[0113] For another example, based on the approval opinions of architects during the analysis of business requirement documents, the keywords in the business keyword library and their relationships with business architecture assets can be iteratively optimized. Among them, the architect can conduct an expert judgment session based on the business requirement document and the analysis result of the business architecture impact scope automatically generated by the system, and issue expert opinions.

[0114] In some embodiments, the above method may further include the following operations.

[0115] First, send associated assets corresponding to the input information to the client. This facilitates the review of the associated assets by experts such as architects.

[0116] Then, receive update information from the client regarding the associated assets corresponding to the input information. For example, the architect can enter expert opinions on the client, such as correct, incorrect, modification opinions, etc.

[0117] Next, if the logged-in account of the client has the permission to maintain the keyword library, update the keyword library based on the update information. The system automatically optimizes and improves the business architecture impact scope according to the expert opinions to continuously iteratively optimize the system. This can also ensure that only users with permissions can maintain the keyword library, reducing asset losses caused by improper operations of architecture assets.

[0118] In some embodiments, determining derivative associated assets for directly associated assets based on the association relationship between multiple assets in the business architecture may include the following operations: If the directly associated assets include common tasks, expand the directly associated assets a specified number of times based on the association relationship between assets in the business architecture to obtain the derivative associated assets of the directly associated assets.

[0119] Figure 6 Schematically shows a schematic diagram of providing business services according to an embodiment of the present disclosure.

[0120] As Figure 6 shown, the process model, product model, market model, and entity model in the business architecture are a method of top-down structured description of enterprise business, and there is a certain association relationship between them through the connection of enterprise architecture logic.

[0121] For example, the process model consists of a set of activities of complete value streams that reflect the enterprise value creation process and is an external manifestation of the enterprise service ability. Taking banking business as an example, the process model generally includes, but is not limited to: deposits, personal loans, wealth management, etc.

[0122] For example, a product model is a method that defines the standard structure of an enterprise's products and arranges business processing flows and rules through product conditions to achieve rapid product configuration. The product model includes product lines, product groups, basic products, salable products, product conditions, etc. Taking banking business as an example, the product model may include, but is not limited to, debit cards, credit cards, quasi-credit cards, etc.

[0123] For example, a market model is a classification and feature model of market variable factors such as enterprise customers and channels, and is the object and channel for the enterprise's core capabilities to be transformed into service outputs.

[0124] For example, an entity model is the capabilities and resources within an enterprise, used to support processes and products in providing business services externally.

[0125] For example, the business architecture also includes one-to-one or one-to-many association relationships between components at each level in the above four major models. In the present invention, the association relationships between businesses, the business architecture, and between business architectures refer to the association relationships between components and between components in the enterprise business, process model, entity model, product model, and market model.

[0126] For example, if a small section in the "Main Content of Requirements" chapter of a requirements document hits a certain task, automatically retrieve whether the task is a public task. If so, automatically retrieve the business component to which the task belongs, and establish an impact association relationship between the business component and the small requirements section.

[0127] In addition, based on the corresponding relationships between assets in the business architecture, the matching assets can be expanded externally a specified number of times to obtain the assets to be recommended. Among them, 1-time expansion can mean taking the assets directly associated with the matching assets as matching assets. 2-time expansion can mean taking the assets directly associated with the matching assets as the first-time expanded assets, and the assets directly associated with the first-time expanded assets as the second-time expanded assets, and taking the matching assets, the first-time expanded assets, and the second-time expanded assets together as the assets to be recommended. Among them, the direct association relationship can be determined based on the existing association relationships in the business architecture.

[0128] This can effectively reduce the probability of missing assets, and will not circle too many assets, improving the accuracy of asset positioning.

[0129] In some embodiments, if the corresponding relationship between the business architecture and the IT architecture has been constructed, assets in the IT architecture, such as code resources, can be located through keywords. For example, in addition to the above corresponding relationships, the associated code assets, etc. can also be determined according to the corresponding relationships between models within the business architecture and the corresponding relationships between services within the IT architecture.

[0130] Figure 7A schematic diagram showing the correspondence between assets in the business architecture and assets in the Internet architecture according to an embodiment of the present disclosure.

[0131] As Figure 7 shown, the IT architecture includes use cases, application transaction services, application component services, and business object services. Figure 7 Where m and n are positive integers greater than or equal to 1, and the values of multiple n can be different.

[0132] The correspondence includes at least one of the following: a one-to-many first sub-correspondence between a task group model and an application transaction service, a one-to-many second sub-correspondence between a task component model and an application component service, and a one-to-many third sub-correspondence between a data structure model and a business object service.

[0133] Among them, a use case may include an interface, navigation, input / output components, and a call relationship between the use case and the application transaction service. Specifically, the use case starts from the task group (i.e., the task group model) + use case in the business architecture, and through the interface, navigation, input / output components, and the call relationship between the use case and the application transaction service, describes the interaction process of a role and a physical application to complete a business function.

[0134] The application transaction service realizes the business function by calling the application component service and outputs the processing result of the business function to the use case. Among them, one application transaction service corresponds to an interaction action between an entity and a service. For example, one application transaction service corresponds to an interaction action between a role and a physical application.

[0135] The application component service is used to assemble the business object service for the application transaction service to call. Specifically, the application component service assembles the business object service and non-business-related technical platform functions within the physical application and exposes the service for the application transaction service to call.

[0136] The business object service is used to encapsulate business rules to perform read operations and / or write operations on data related to entities in a data set. Specifically, the business object service encapsulates business rules, operates on business objects, decouples business logic from data access, and operates on the database through a technical framework.

[0137] In some embodiments, the keyword library may further include a second correspondence between assets in the business architecture and assets in the Internet architecture.

[0138] Correspondingly, the above method may further include the following operations.

[0139] After determining the associated assets associated with the keyword, based on the correspondence between the assets in the business architecture and the assets in the IT architecture, obtain the Internet assets associated with the keyword from the Internet architecture. Then, send the Internet assets to the client. Among them, the Internet assets include, but are not limited to: code resources, database data resources related to the code resources, etc.

[0140] After determining the associated assets associated with the keyword in the business architecture in the embodiments of the present disclosure, based on the correspondence between the assets in the business architecture and the assets (including computer programs, transaction codes, database data tables, etc.) in the IT architecture, determine the associated assets in the IT architecture corresponding to the associated assets in the business architecture. In the above manner, code assets, entity assets, etc. related to the new project can be conveniently and accurately determined.

[0141] For example, the user submits a standardized business requirements document in the system. The system retrieves the enterprise business service keywords according to the relevant chapters of the requirements document, automatically retrieves the associated assets involved in the business architecture according to the keywords, and outputs the associated assets.

[0142] The finally output business architecture impact analysis report includes, such as, standardized requirement document name, business requirements, business fields, asset types, business components, architecture adjustment classification, etc., and will be automatically issued to the user and the architect by the system respectively.

[0143] The results output by the server side can be as shown in Table 1, and will be automatically issued to the user who initiates the search and / or the architect by the system respectively.

[0144] Table 1

[0145]

[0146] In the embodiments of the present disclosure, for the standardized and normalized business requirements document, by establishing the correspondence between the keywords involved in the enterprise business service and the assets of the business architecture, automatically analyze the assets of the business architecture involved in the business requirements document, reduce the high requirements for the analyst's mastery and application ability of the business architecture knowledge. The user only needs to submit the business requirements document normally, and the business architecture impact scope of the business requirements can be intelligently delineated.

[0147] Another aspect of the present disclosure provides an associated asset determination device.

[0148] Figure 8 Schematically shows a block diagram of the associated asset determination device according to an embodiment of the present disclosure.

[0149] As Figure 8 shown, the associated asset determination device may include: an input information acquisition module 810, a direct associated asset determination module 820, and a derivative associated asset determination module 830.

[0150] Among them, the input information acquisition module 810 is used to acquire input information from the client.

[0151] The directly associated asset determination module 820 is configured to, in response to the input information, perform matching in the keyword library based on the input information to determine directly associated assets corresponding to the input information. The keyword library includes the corresponding relationships between assets and keywords in the business architecture.

[0152] The derivative associated asset determination module 830 is used to determine derivative associated assets for the directly associated assets based on the association relationships between multiple assets in the business architecture.

[0153] Figure 9 Schematically shows a schematic diagram of the architecture asset analysis process according to an embodiment of the present disclosure.

[0154] As Figure 9 shown, the user submits input information (which may include search statements entered by the user in the search engine; documents such as project application, project plan, architecture analysis, and requirement use cases) to the server side through the client, so that the server side can extract keywords from the input information for matching and analysis in the keyword library. For the keywords that match successfully, the associated assets (i.e., the influence scope of the business architecture) can be determined based on the corresponding relationships between the keywords and the assets in the business architecture. This can effectively reduce the dependence of R & D personnel on their own reserved business architecture knowledge.

[0155] In addition, professional personnel such as architects can conduct expert review on the associated assets determined by the keywords, so as to optimize and update the constructed keyword library based on the review results. Or, adjust the associated assets according to the architect review opinion conditions, send the adjusted associated assets to the architect for re-review, or output the adjusted associated assets to the user in the form of a business architecture impact analysis report, etc.

[0156] It should be noted that the implementation manners, technical problems solved, functions achieved, and technical effects achieved by each module / unit, etc. in the device part embodiments are the same or similar to those of the corresponding steps in the method part embodiments respectively, and will not be elaborated herein one by one.

[0157] Any number of modules or units according to embodiments of the present disclosure, or at least part of the functions of any of them, may be implemented in one module. Any one or more of the modules or units according to embodiments of the present disclosure may be split into multiple modules for implementation. Any one or more of the modules or units according to embodiments of the present disclosure may 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-chip, a system-on-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or may be implemented by any other reasonable manner of integrating or packaging circuits, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, one or more of the modules or units according to embodiments of the present disclosure may be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0158] For example, any number of the input information acquisition module 810, the directly associated asset determination module 820, and the derived associated asset determination module 830 may be combined and implemented in one module, or any one of them may be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules may be combined with at least part of the functions of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the input information acquisition module 810, the directly associated asset determination module 820, and the derived associated asset determination module 830 may 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-chip, a system-on-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or may be implemented by any other reasonable manner of integrating or packaging circuits, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, at least one of the input information acquisition module 810, the directly associated asset determination module 820, and the derived associated asset determination module 830 may be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0159] Figure 10 A block diagram of an electronic device according to an embodiment of the present disclosure is schematically shown. Figure 10 The illustrated electronic device is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present disclosure.

[0160] Such as Figure 10As shown, the electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application-specific integrated circuit (ASIC)), and so on. The processor 1001 may also include on-board memory for caching purposes. The processor 1001 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.

[0161] In the RAM 1003, various programs and data required for the operation of the electronic device 1000 are stored. The processor 1001, the ROM 1002, and the RAM 1003 are communicatively connected to each other via a bus 1004. The processor 1001 performs various operations of the method flow according to an embodiment of the present disclosure by executing the programs in the ROM 1002 and / or the RAM 1003. It should be noted that the program may also be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 may also perform various operations of the method flow according to an embodiment of the present disclosure by executing the programs stored in one or more memories.

[0162] According to an embodiment of the present disclosure, the electronic device 1000 may further include an input / output (I / O) interface 1005, and the input / output (I / O) interface 1005 is also connected to the bus 1004. The electronic device 1000 may further include one or more of the following components connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed so that a computer program read from it can be installed into the storage section 1008 as needed.

[0163] According to an embodiment of the present disclosure, the method flow according to the embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the processor 1001, the above functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the above-described system, device, apparatus, module, unit, etc. can be implemented by computer program modules.

[0164] The present disclosure also provides a computer-readable storage medium, which may be included in the device / device / system described in the above embodiment; or may exist separately without being assembled into the device / device / 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.

[0165] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: portable computer disks, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the above-described ROM 1002 and / or RAM 1003 and / or one or more memories other than ROM 1002 and RAM 1003.

[0166] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program includes program codes for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program codes are used to cause the electronic device to implement the image model training method or image processing method provided by the embodiment of the present disclosure.

[0167] When the computer program is executed by the processor 1001, the above functions defined in the system / apparatus of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the above-described system, apparatus, module, unit, etc. can be implemented by computer program modules.

[0168] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. 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 section 1009, and / or installed from the removable medium 1011. The program code included in the computer program may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0169] According to embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure may be written in any combination of one or more programming languages. Specifically, these computing programs may be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include but are not limited to, such as Java, C++, python, the "C" language, or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may 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 may be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0170] Those skilled in the art can understand that the features recited in the various embodiments and / or claims of the present disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly recited in the present disclosure. These embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the above embodiments have been described separately, this does not mean that the measures in each embodiment cannot be used advantageously in combination. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and these substitutions and modifications should all fall within the scope of the present disclosure.

Claims

1. An associated asset determination method executed by a server side, comprising: obtaining input information from a client; in response to the input information, performing a match in a keyword library based on the input information to determine a directly associated asset corresponding to the input information, where the keyword library includes the corresponding relationship between assets and keywords in a business architecture, wherein the construction method of the keyword library includes at least one of the following: associatively storing keywords from a client and assets of a business architecture in a specified storage space; or expanding the corresponding relationship in the keyword library based on the corresponding relationship between existing assets and keywords and the corresponding relationship between assets, where the corresponding relationship between assets includes a one-to-one or one-to-many association relationship between components at each level of a model in a business architecture, wherein the keyword library further includes the corresponding relationship between assets and keyword groups in a business architecture; and determining a derivative associated asset for the directly associated asset based on the association relationship between multiple assets in a business architecture, wherein the input information includes a chapter name, and the chapter name includes a phrase composed of a verb and a noun; the performing a match in a keyword library based on the input information to determine a directly associated asset corresponding to the input information includes: extracting the chapter name from the input information; and using the phrase in the chapter name to perform a match in the keyword library to determine a directly associated asset corresponding to the chapter name, wherein the determining a derivative associated asset for the directly associated asset based on the association relationship between multiple assets in a business architecture includes: if the directly associated asset includes a common task, expanding the directly associated asset a specified number of times based on the association relationship between assets in the business architecture to obtain a derivative associated asset of the directly associated asset.

2. The method according to claim 1, wherein, the using the phrase in the chapter name to perform a match in a keyword library to determine a directly associated asset corresponding to the chapter name includes: performing word segmentation on the chapter name to obtain a word segmentation result; taking the whole of adjacent verbs and nouns in the word segmentation result as a keyword group; and using the keyword group to perform a match in the keyword library to obtain a directly associated asset corresponding to the keyword group.

3. The method according to claim 1, wherein, the input information includes chapter content; the performing a match in a keyword library based on the input information to determine a directly associated asset corresponding to the input information includes: obtaining summary information of the chapter content; extracting keywords from the summary information; and using the keywords to perform a match in the keyword library to obtain a directly associated asset corresponding to the keywords.

4. The method according to claim 3, wherein, the extracting keywords from the summary information includes: extracting keywords from the summary information based on at least one of word frequency, word importance, page rank or word vectors.

5. The method according to claim 3, wherein, The keyword has a level, and the level of the keyword is determined based on the accuracy of the associated assets of the keyword; The method further includes: when multiple keywords are obtained, screening the associated assets from the directly associated assets corresponding to the multiple keywords based on the levels of the keywords.

6. The method according to claim 5, wherein, the indicators of the accuracy include at least one of the frequency of user use and the adoption rate; The method further includes: obtaining historical user data through buried point records; determining at least one of the frequency of user use and the adoption rate from the historical user data; and updating the keyword library based on at least one of the frequency of user use and the adoption rate.

7. The method according to claim 5, wherein, the associated assets of the low-level keywords are enclosed within the scope of the associated assets of the high-level keywords.

8. The method according to claim 1, further includes: if the chapter name includes a specified field, discarding the chapter content corresponding to the chapter name.

9. The method according to any one of claims 1 to 8, further includes: sending the associated assets corresponding to the input information to the client; receiving update information from the client for the associated assets corresponding to the input information; and and if the login account of the client has the authority to maintain the keyword library, updating the keyword library based on the update information.

10. An electronic device, including: one or more processors; a storage device for storing executable instructions, and when the executable instructions are executed by the processor, implementing the method according to any one of claims 1 to 9.

11. A computer-readable storage medium storing computer-executable instructions, and when the executable instructions are executed by a processor, implementing the method according to any one of claims 1 to 9.

Images