Business architecture positioning method and device, electronic equipment, medium and program product

Abstract

The present disclosure provides a business architecture positioning method, device, electronic equipment, medium and computer program product. The above method and device can be used in the field of artificial intelligence technology. The business architecture positioning method comprises: performing a word segmentation operation on obtained business requirement information of a business requirement to obtain m core words in the business requirement information, wherein m is an integer greater than or equal to 1; matching the m core words with a pre-constructed keyword library to determine k keywords corresponding to the m core words, wherein the keyword library comprises n keywords, f business architectures and a mapping relationship between the keywords and the business architectures, wherein k, n and f are all integers greater than or equal to 1, k is less than n, and n is greater than or equal to f; positioning the business architectures associated with the k keywords corresponding to the m core words according to the mapping relationship; and applying the associated business architectures as the business architecture of the business requirement.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and more specifically, to a positioning method, apparatus, electronic device, medium, and computer program product for a business architecture. Background Technology

[0002] As business architecture plays an increasingly important role in enterprise business development, its content is also becoming more complex. Accurately identifying and applying the required business architecture during business innovation is becoming a core element in reducing business architecture maintenance. The most effective way to enhance the value of business architecture is to accurately identify and apply it to business innovation, thereby managing its maintenance through innovation. Therefore, accurately identifying the business architecture based on the needs of business innovation has become a critical bottleneck problem that urgently needs to be solved. Summary of the Invention

[0003] In view of this, the present disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for locating a business architecture that is easy to implement and can accurately locate the required business architecture.

[0004] One aspect of this disclosure provides a method for locating a business architecture, comprising: performing word segmentation on acquired business requirement information to obtain m core words in the business requirement information, where m is an integer greater than or equal to 1; matching the m core words with a pre-built keyword library to determine k keywords corresponding to the m core words, wherein the keyword library includes n keywords, f business architectures, and mapping relationships between keywords and business architectures, where k, n, and f are all integers greater than or equal to 1, and k is less than n, and n is greater than or equal to f; locating the business architecture associated with the k keywords corresponding to the m core words according to the mapping relationships; and applying the associated business architecture as the business architecture of the business requirement.

[0005] According to the business architecture positioning method of this disclosure, based on m core words in business requirement information, k keywords corresponding to the m core words can be determined by matching the m core words with a pre-built keyword library. Since the keyword library includes n keywords, f business architectures, and the mapping relationship between keywords and business architectures, the business architecture associated with the k keywords corresponding to the m core words can be located according to the mapping relationship. Furthermore, the associated business architecture can be applied as the business architecture required by the business requirements. This positioning method solves the problem of enterprises being unable to accurately locate the required business architecture during business innovation, and it is easy to implement and has good positioning accuracy.

[0006] In some embodiments, the step of matching the core words with a pre-built keyword library to determine k keywords corresponding to the m core words includes: performing k-means clustering on the m core words according to a pre-set number of clusters k to obtain k matching words; and calculating the similarity between each matching word and each keyword in the n keywords in the keyword library using a similarity calculation method, and taking the keyword corresponding to the maximum similarity value as the keyword corresponding to the matching word.

[0007] In some embodiments, locating the business architecture associated with k keywords corresponding to the m core words based on the mapping relationship includes: locating x pre-selected business architectures for the business requirement using the mapping relationship of each of the k keywords, where x is an integer greater than or equal to 1; calculating the degree of association between each pre-selected business architecture and the business requirement, where the degree of association is calculated based on the closeness between the keywords with mapping relationships and the business architecture; and ranking the pre-selected business architecture corresponding to the highest degree of association from largest to smallest, and taking the pre-selected business architecture with the highest degree of association as the associated business architecture.

[0008] In some embodiments, the step of locating x pre-selected business architectures for the business requirement using the mapping relationship of each of the k keywords includes: locating the g of each keyword using the mapping relationship of the k keywords. i A pre-selected business architecture, of which g i Let be an integer greater than or equal to 1, and let i be an integer greater than or equal to 1 and less than or equal to k; and let g1 + g2 + g3 + ... + g i +......+g k If the same business architecture is among the pre-selected business architectures, it will be merged into one, resulting in x pre-selected business architectures.

[0009] In some embodiments, calculating the degree of association between each of the pre-selected business architectures and the business requirement includes: when the pre-selected business architecture is a merged pre-selected business architecture, the degree of association between the pre-selected business architecture and the business requirement is the sum of the closeness between all pre-selected business architectures before the merger and their corresponding keywords; and when the pre-selected business architecture is an unmerged pre-selected business architecture, the degree of association between the pre-selected business architecture and the business requirement is the closeness between the pre-selected business architecture and its corresponding keywords.

[0010] In some embodiments, the pre-construction of the keyword library includes: performing word segmentation on the architecture information of f business architectures to obtain n keywords, wherein the architecture information includes architecture name, architecture definition and architecture rules; and storing the n keywords, the f business architectures and the mapping relationship between the keywords and the business architectures in the keyword library, wherein the mapping relationship has a proximity attribute, and the proximity attribute is the proximity between the keywords with the mapping relationship and the business architecture.

[0011] In some embodiments, the method for determining the affinity between keywords with mapping relationships and business architecture includes: setting the affinity between keywords obtained by segmenting the architecture name and the corresponding business architecture as 'a', setting the affinity between keywords obtained by segmenting the architecture definition and the corresponding business architecture as 'b', and setting the affinity between keywords obtained by segmenting the architecture rules and the corresponding business architecture as 'c', where 0 < a < 1, 0 < b < 1, 0 < c < 1, a > b > c.

[0012] Another aspect of this disclosure provides a business architecture positioning device, comprising: a word segmentation module, which performs word segmentation on acquired business requirement information to obtain m core words in the business requirement information, where m is an integer greater than or equal to 1; a determination module, which performs matching of the m core words with a pre-built keyword library to determine k keywords corresponding to the m core words, wherein the keyword library includes n keywords, f business architectures, and mapping relationships between keywords and business architectures, where k, n, and f are all integers greater than or equal to 1, and k is less than n, and n is greater than or equal to f; a positioning module, which performs positioning to locate the business architecture associated with the k keywords corresponding to the m core words according to the mapping relationship; and an application module, which performs application to use the associated business architecture as the business architecture of the business requirement.

[0013] Another aspect of this disclosure provides an electronic device including one or more processors and one or more memories, wherein the memories are used to store executable instructions that, when executed by the processor, implement the method described above.

[0014] Another aspect of this disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, are used to implement the method described above.

[0015] Another aspect of this disclosure provides a computer program product including a computer program comprising computer executable instructions that, when executed, implement the method described above. Attached Figure Description

[0016] The above and other objects, features and advantages of this disclosure will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0017] Figure 1 This illustration schematically shows an exemplary system architecture to which methods and apparatus can be applied according to embodiments of the present disclosure;

[0018] Figure 2 A flowchart illustrating a method for locating a business architecture according to an embodiment of this disclosure is shown schematically.

[0019] Figure 3 A flowchart illustrating a pre-built keyword library according to an embodiment of the present disclosure is shown schematically;

[0020] Figure 4 The flowchart illustrates an embodiment of the present disclosure of a process for matching core words with a pre-built keyword library to determine k keywords corresponding to m core words.

[0021] Figure 5 This schematically illustrates a flowchart of a business architecture based on a mapping relationship, according to an embodiment of the present disclosure, for locating the association between k keywords corresponding to m core words;

[0022] Figure 6 The flowchart illustrates a process for locating x pre-selected business architectures for business requirements using the mapping relationship of each of k keywords, according to an embodiment of the present disclosure.

[0023] Figure 7 A flowchart illustrating the correlation between each pre-selected service architecture and service requirements is shown schematically according to an embodiment of this disclosure;

[0024] Figure 8 The diagram illustrates the correspondence between matching words and pre-selected business architecture according to embodiments of the present disclosure.

[0025] Figure 9 A schematic diagram illustrating the nesting of algorithms according to embodiments of the present disclosure is shown.

[0026] Figure 10 A block diagram of a positioning device illustrating a business architecture according to an embodiment of the present disclosure is shown schematically.

[0027] Figure 11 A block diagram of an electronic device according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0028] The embodiments of the present disclosure will now 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 disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0029] In the technical solution disclosed herein, the acquisition, storage, and application of user personal information all comply with relevant laws and regulations, necessary confidentiality measures have been taken, and there is no violation of public order and good morals. In the technical solution disclosed herein, the acquisition, collection, storage, use, processing, transmission, provision, disclosure, and application of data all comply with relevant laws and regulations, necessary confidentiality measures have been taken, and there is no violation of public order and good morals.

[0030] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0031] When using expressions such as "at least one of A, B, or C," it should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (e.g., "a system having at least one of A, B, or C" should include, but is not limited to, systems having A alone, having B alone, having C alone, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.). The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, features defined with "first" or "second" may explicitly or implicitly include one or more of the stated features.

[0032] As business architecture plays an increasingly important role in enterprise business development, its content is also becoming more complex. Accurately identifying and applying the required business architecture during business innovation is becoming a core element in reducing business architecture maintenance. The most effective way to enhance the value of business architecture is to accurately identify and apply it to business innovation, thereby managing its maintenance through innovation. Therefore, accurately identifying the business architecture based on the needs of business innovation has become a critical bottleneck problem that urgently needs to be solved.

[0033] Embodiments of this disclosure provide a method, apparatus, electronic device, computer-readable storage medium, and computer program product for locating business architecture. The method for locating business architecture includes: performing word segmentation on acquired business requirement information to obtain m core words from the business requirement information, where m is an integer greater than or equal to 1; matching the m core words with a pre-built keyword library to determine k keywords corresponding to the m core words, wherein the keyword library includes n keywords, f business architectures, and mapping relationships between keywords and business architectures, where k, n, and f are all integers greater than or equal to 1, and k is less than n, and n is greater than or equal to f; locating the business architecture associated with the k keywords corresponding to the m core words based on the mapping relationships; and applying the associated business architecture as the business architecture of the business requirement.

[0034] It should be noted that the positioning methods, devices, electronic devices, computer-readable storage media, and computer program products of the business architecture disclosed herein can be used in the field of artificial intelligence technology, or in any field other than the field of artificial intelligence technology, such as the financial field. The field of this disclosure is not limited here.

[0035] Figure 1 This illustration schematically depicts an exemplary system architecture 100 for positioning methods, apparatuses, electronic devices, computer-readable storage media, and computer program products that can be applied to business architectures according to embodiments of this disclosure. It should be noted that... Figure 1 The examples shown are merely examples of system architectures that can be applied to the embodiments of this disclosure, in order to help those skilled in the art understand the technical content of this disclosure, but do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments or scenarios.

[0036] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the terminal devices 101, 102, and 103 and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0037] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0038] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0039] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using terminal devices 101, 102, and 103 (for example only). The backend management server can analyze and process data such as received 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 devices.

[0040] It should be noted that the business architecture positioning method provided in this embodiment can generally be executed by server 105. Correspondingly, the business architecture positioning device provided in this embodiment can generally be located in server 105. The business architecture positioning method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the business architecture positioning device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0041] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0042] The following will be based on Figure 1 The described scene, through Figures 2-8 The method for locating the business architecture according to the embodiments of this disclosure will be described in detail.

[0043] Figure 2 A flowchart illustrating a location method for a business architecture according to an embodiment of this disclosure is shown schematically.

[0044] like Figure 2 As shown, the business architecture positioning method of this embodiment includes operations S210 to S240.

[0045] In operation S210, the acquired business requirement information is segmented into words to obtain m core words, where m is an integer greater than or equal to 1. For example, word segmentation technology can be used to process the business requirement information to obtain m core words.

[0046] In operation S220, the m core words are matched with the pre-built keyword library to determine the k keywords corresponding to the m core words. The keyword library includes n keywords, f business architectures and the mapping relationship between keywords and business architectures. k, n and f are all integers greater than or equal to 1, and k is less than n and n is greater than or equal to f.

[0047] As one possible way to achieve this, such as Figure 3 As shown, the pre-built keyword library includes operations S310 and S320.

[0048] In operation S310, the architecture information of the acquired f business architectures is segmented into words to obtain n keywords. The architecture information includes the architecture name, architecture definition, and architecture rules. For example, word segmentation technology can be used to process the architecture name, architecture definition, and architecture rules of each of the f business architectures to obtain n keywords. Here, the f business architectures can be understood as the names of the f business architectures.

[0049] In operation S320, n keywords, f business architectures, and the mapping relationship between keywords and business architectures are stored in the keyword library. The mapping relationship has a proximity attribute, which is the proximity between the keywords with the mapping relationship and the business architecture.

[0050] The following examples illustrate operations S310 and S320 using specific business architecture examples, such as the business architecture for domestic deposits. This architecture is defined as processing and managing deposits for users who have opened accounts domestically. The architecture rules are as follows: Rule 1: When processing a deposit, the original ID card information and a completed deposit slip are required. The information filled in on the deposit slip includes the user's name, address, occupation, and deposit amount, etc.; Rule 2: The interest rate for a one-year term deposit of less than 500,000 is 3%; the interest rate for a one-year term deposit of 500,000 to 1,000,000 is 4%; and the interest rate for a one-year term deposit of more than 1,000,000 is 5%, etc.

[0051] By segmenting the business structure of domestic deposits using word segmentation technology, specifically by segmenting "domestic deposits," "deposit processing and management for users who have opened accounts domestically," and "Rule 1: When making a deposit, the original ID card information and a deposit slip must be filled out. The information filled out by the user on the deposit slip includes the user's name, address, occupation, and deposit amount, etc.; Rule 2: The interest rate for a one-year term is 3% for deposits less than 500,000, 4% for deposits between 500,000 and 1,000,000, and 5% for deposits greater than 1,000,000, etc.," we can obtain the keywords "domestic," "deposit," "account opening," "user," "deposit processing," "deposit management," "original ID card," "filling out a deposit slip," "deposit slip user information," "user's name," "user's address," "user's occupation," "deposit amount," "less than," "500,000," "one-year term," "interest rate of 3%," "500,000 to 1,000,000," "interest rate of 4%," "greater than 1,000,000," and "interest rate of 5%."

[0052] Therefore, the keywords "domestic," "deposit," "account opening," "user," "deposit processing," "deposit management," "original ID card," "filling out a deposit slip," "deposit slip user information," "user name," "user address," "user occupation," "deposit amount," "less than," "500,000," "one-year term," "interest rate of 3%," "500,000 to 1,000,000," "interest rate of 4%," "greater than 1,000,000," and "interest rate of 5%" can be stored in the keyword library. The business structure of domestic deposits can also be stored in the keyword library. All the keywords "domestic," "deposit," "account opening," "user," "deposit processing," "deposit management," "original ID card," "filling out a deposit slip," "deposit slip user information," "user name," "user address," "user occupation," "deposit amount," "less than," "500,000," "one-year term," "interest rate of 3%," "500,000 to 1,000,000," "interest rate of 4%," "greater than 1,000,000," and "interest rate of 5%" have a mapping relationship with the business structure of domestic deposits. Therefore, it is necessary to store the mapping relationship between each of the above keywords and the business structure of domestic deposits in the keyword library.

[0053] The aforementioned business structure for domestic deposits is merely illustrative and should not be construed as a limitation of this disclosure. The f business structures mentioned in this disclosure can be of any type and are not limited to the business structure for domestic deposits. Performing the above operation on a specific business structure will yield at least one keyword, and performing the above operation on each of the f business structures will yield n keywords. There may be duplicate keywords among different business structures within the f business structures; therefore, the same keyword may have a mapping relationship with different business structures, and the affinity between the keyword and different business structures may be the same or different.

[0054] By operating S310 and S320, it is easy to pre-build a keyword library.

[0055] It should be noted that the mapping relationship has a proximity attribute, which represents the closeness between the keywords with the mapping relationship and the business architecture. The closeness between keywords and the business architecture can be determined by proximity rules. Specifically, these rules can include: keywords obtained from segmentation of the architecture name have a greater closeness to the business architecture than keywords obtained from segmentation of the architecture definition, which in turn have a greater closeness than keywords obtained from segmentation of the architecture rules.

[0056] In some specific examples, the method for determining the affinity between keywords with mapping relationships and business architecture includes: setting the affinity between keywords obtained by segmenting the architecture name and their corresponding business architecture as 'a', setting the affinity between keywords obtained by segmenting the architecture definition and their corresponding business architecture as 'b', and setting the affinity between keywords obtained by segmenting the architecture rules and their corresponding business architecture as 'c', where 0 < a < 1, 0 < b < 1, 0 < c < 1, a > b > c. This facilitates determining the affinity between the n keywords obtained from the architecture information of the business architecture and the business architecture. Having affinity between the n keywords and the business architecture makes subsequent operation S230 more accurate in locating the business architecture associated with the k keywords corresponding to the m core words based on the mapping relationship.

[0057] In operation S230, the business architecture associated with k keywords corresponding to m core words is located based on the mapping relationship.

[0058] When operating S240, the associated business architecture is applied as the business architecture for business requirements.

[0059] According to the business architecture positioning method of this disclosure, based on m core words in business requirement information, k keywords corresponding to the m core words can be determined by matching the m core words with a pre-built keyword library. Since the keyword library includes n keywords, f business architectures, and the mapping relationship between keywords and business architectures, the business architecture associated with the k keywords corresponding to the m core words can be located according to the mapping relationship. Furthermore, the associated business architecture can be applied as the business architecture required by the business requirements. This positioning method solves the problem of enterprises being unable to accurately locate the required business architecture during business innovation, and it is easy to implement and has good positioning accuracy.

[0060] Figure 4 The illustration shows a flowchart of matching core words with a pre-built keyword library to determine k keywords corresponding to m core words, according to an embodiment of the present disclosure.

[0061] Operation S220 matches the core words with a pre-built keyword library to determine the k keywords corresponding to the m core words, including operations S221 and S222.

[0062] In operation S221, k-means clustering is performed on m core words according to the pre-set number of clusters k, resulting in k matching words.

[0063] In operation S222, the similarity calculation method is used to calculate the similarity between each matching word and each of the n keywords in the keyword library, and the keyword corresponding to the maximum similarity value is taken as the keyword corresponding to the matching word.

[0064] Understandably, when performing k-means clustering on m core words, each core word needs to be converted into a word vector. Therefore, the resulting K matching words can be word vectors. Similarly, each of the n keywords in the keyword library can be converted into a word vector. Then, similarity calculation methods, such as the cosine similarity algorithm, can be used to calculate the similarity between each matching word and each of the n keywords in the keyword library, resulting in n similarity values. Ranking these n similarity values ​​from largest to smallest allows us to find the highest similarity value. The keyword corresponding to the highest similarity value is then taken as the keyword corresponding to that matching word. Thus, operations S221 and S222 facilitate matching core words with a pre-built keyword library to determine the k keywords corresponding to the m core words.

[0065] Figure 5 The flowchart illustrates a business architecture based on a mapping relationship, according to an embodiment of the present disclosure, for locating k keywords associated with m core words.

[0066] Operation S230 locates the business architecture associated with k keywords corresponding to m core words based on the mapping relationship, including operations S231 to S233.

[0067] In operation S231, the mapping relationship of each keyword among the k keywords is used to locate x pre-selected business architectures for the business requirements, where x is an integer greater than or equal to 1.

[0068] As one possible way to achieve this, such as Figure 6 As shown, operation S231 uses the mapping relationship of each keyword among the k keywords to locate x pre-selected business architectures for business requirements, including operation S2311 and operation S2312.

[0069] In operation S2311, the mapping relationship of each of the k keywords is used to locate the g of the keyword. i A pre-selected business architecture, of which g i Let i be an integer greater than or equal to 1, and let i be an integer greater than or equal to 1 and less than or equal to k.

[0070] In operation S2312, g1+g2+g3+......+g i +......+g k Identical business architectures among the pre-selected business architectures are merged into one, resulting in x pre-selected business architectures. Therefore, by operating S2311 and S2312, it is easy to locate the x pre-selected business architectures for the business requirements using the mapping relationship of each of the k keywords.

[0071] In operation S232, the degree of correlation between each pre-selected business architecture and business requirements is calculated, where the degree of correlation is calculated based on the closeness between keywords with mapping relationships and business architectures.

[0072] As an feasible approach, such as Figure 7 As shown, operation S232 calculates the degree of correlation between each pre-selected business architecture and business requirements, including operations S2321 and S2322.

[0073] In operation S2321, when the pre-selected business architecture is the merged pre-selected business architecture, the degree of correlation between the pre-selected business architecture and the business requirements is the sum of the closeness between all the pre-selected business architectures before the merger and their corresponding keywords.

[0074] In operation S2322, when the pre-selected business architecture is an unmerged pre-selected business architecture, the degree of correlation between the pre-selected business architecture and the business requirements is the closeness between the pre-selected business architecture and the corresponding keyword. Therefore, operations S2321 and S2322 can be used to easily calculate the degree of correlation between each pre-selected business architecture and the business requirements.

[0075] In operation S233, the business architectures are ranked from highest to lowest relevance, and the pre-selected business architecture corresponding to the highest relevance is taken as the associated business architecture. Therefore, by operating S231 to S233, it is easy to locate the business architecture associated with k keywords corresponding to m core keywords based on the mapping relationship.

[0076] The following is for reference. Figure 8 The correspondence between the matching words and the pre-selected business architecture is exemplarily described in operations S231 to S233. Figure 8 This is merely an example and should not be construed as a limitation of this disclosure.

[0077] Assuming the number of clusters, k, is set to 3, k-means clustering yields 3 matching terms: matching term A, matching term B, and matching term C. Using similarity calculation methods, we can find keyword A corresponding to matching term A, keyword B corresponding to matching term B, and keyword C corresponding to matching term C in the keyword database.

[0078] In the keyword database, the business architectures mapped to keyword A include business architecture a, business architecture b, and business architecture c. The affinity between keyword A and business architecture a is W1, the affinity between keyword A and business architecture b is W2, and the affinity between keyword A and business architecture c is W3. The business architectures mapped to keyword B include business architecture a and business architecture d. The affinity between keyword B and business architecture a is W4, and the affinity between keyword B and business architecture d is W5. The business architectures mapped to keyword C include business architecture a, business architecture c, business architecture d, and business architecture e. The affinity between keyword C and business architecture a is W6, the affinity between keyword C and business architecture c is W7, the affinity between keyword C and business architecture d is W8, and the affinity between keyword C and business architecture e is W9.

[0079] Therefore, by using the mapping relationship between keywords A, B, and C, five pre-selected business architectures for the business requirements can be identified, namely business architecture a, business architecture b, business architecture c, business architecture d, and business architecture e.

[0080] Specifically, keyword A identifies three pre-selected business architectures: architecture a, architecture b, and architecture c. Keyword B identifies two pre-selected business architectures: architecture a and architecture d. Keyword C identifies four pre-selected business architectures: architecture a, architecture c, architecture d, and architecture e. Among the multiple pre-selected business architectures identified by keywords A, B, and C, architectures a, c, and d are duplicates. Therefore, the three architectures a are merged into one architecture a as one pre-selected business architecture, the two architectures c are merged into one architecture c as one pre-selected business architecture, and the two architectures d are merged into one architecture d as one pre-selected business architecture. This results in five pre-selected business architectures corresponding to the business requirements: architecture a, architecture b, architecture c, architecture d, and architecture e.

[0081] Since the pre-selected business architecture a is the merged business architecture, the degree of correlation between the pre-selected business architecture a and the business requirements is the sum of the affinity between the pre-selected business architecture a and keyword A (W1), the affinity between the pre-selected business architecture a and keyword B (W4), and the affinity between the pre-selected business architecture a and keyword C (W6). In other words, the degree of correlation between the pre-selected business architecture a and the business requirements is W1 + W4 + W6.

[0082] Since the pre-selected business architecture b is an unmerged business architecture, the degree of correlation between the pre-selected business architecture b and the business requirements is the affinity W2 between the pre-selected business architecture b and keyword A.

[0083] Similarly, the degree of correlation between the pre-selected business architecture c, pre-selected business architecture d, and pre-selected business architecture e and the business requirements can be calculated, which will not be elaborated here.

[0084] By ranking the businesses by their degree of relevance from highest to lowest, the pre-selected business architecture corresponding to the highest degree of relevance can be used as the relevant business architecture.

[0085] The following reference Figure 9 The positioning method of the business architecture according to embodiments of this disclosure is described in detail. It is worth understanding that the following description is merely illustrative and not a specific limitation of this disclosure.

[0086] The business architecture localization method according to embodiments of this disclosure combines two different algorithms: one uses a proximity method to form a keyword library based on the business architecture and business model, and the other uses a K-means clustering algorithm to analyze the relationship between business requirements and keywords, starting from the business requirement specification to be analyzed, thereby analyzing the relationship between the business requirement specification and the business architecture and business model. This combination not only solves the problem of business architecture analysis for innovative businesses but also improves the accuracy of business architecture analysis.

[0087] like Figure 9 As shown, this disclosure achieves a more accurate analysis through algorithm nesting. Algorithm nesting includes an inner-loop algorithm and an outer-loop algorithm. The inner-loop algorithm and the outer-loop algorithm are described in detail below.

[0088] Inner Circle Algorithm: Build a keyword library and use proximity methods to improve accuracy.

[0089] (1) The system automatically uses the proximity method to build a basic keyword library: Based on the content of business architecture and business model, the core words are found using word segmentation technology: The core words of the process-related model are composed of "verb + noun", which are then divided into "verb" groups and "noun" groups respectively; the model related to things is composed of "noun".

[0090] Based on the names, definitions, and rules of the business architecture and business model, the core keywords are sorted according to their degree of closeness using a proximity method. Parameter values ​​are set using expert experience to filter out usable keywords. Basic keyword libraries are formed separately for verbs and nouns, and relationships are established between keywords and the business architecture or business model.

[0091] (2) Expand the keyword database

[0092] Based on the keywords in the basic keyword library, synonyms and near-synonyms are added to form an expanded keyword library. The expanded keywords are still associated with the basic keywords in relation to the business architecture and business model.

[0093] Once the keyword library and its association with business architecture and business models are established, multiple keywords are often found for the same business architecture or model. However, the closeness of different keywords to that architecture or model varies. The same keyword is often associated with multiple business architectures or models, but the closeness of the same keyword to different business architectures or models also varies.

[0094] (3) Keyword database usage

[0095] When a keyword is used to query a related business architecture, the system automatically sorts and displays the results in descending order of proximity. When multiple keywords are used to query, the system calculates the proximity of the multiple keywords to the business architecture or business model, and then sorts and displays them in descending order of proximity.

[0096] For example, the keyword "deposit" is associated with many business architectures or models. Combined with keywords like "agreement," "interest," and "withdrawal," each keyword maps to multiple business architectures or models. However, their combination clearly points to a specific business architecture or model. Individual word combinations can also specify a particular business architecture or model, such as deposit, agreement, interest; deposit, agreement, withdrawal; deposit, interest, withdrawal, etc. Then, the most comprehensive keyword combination yields the most accurate business architecture and model. The number of keywords is gradually reduced to obtain matching business architectures and models. For an equal number of keywords, proximity is calculated to determine priority.

[0097] Outer ring algorithm: Extended using K-means clustering algorithm.

[0098] The number of keywords stored in the keyword database remains relatively stable, especially with limited additions when the business architecture remains largely unchanged. However, business innovation leads to exponential growth, particularly in complex business models. Failure to replenish keywords promptly will result in inaccurate business architecture analysis, impacting the usability and updates of the business architecture. Therefore, a K-means clustering algorithm centered on keywords is established to expand the covered business scope and enhance the accuracy of business architecture analysis.

[0099] K-means clustering algorithm:

[0100] Target words: Clustering is performed using keywords from the keyword library as targets.

[0101] K value selection: Based on the requirements, identify the departments and find the number of corresponding basic keywords according to their associated business architecture, and use this number as the cluster size K.

[0102] Analyzed object: The core words in the business description section of the business requirements specification are found using natural language processing algorithms and used for business architecture analysis.

[0103] Calculation logic:

[0104] (1) Use word segmentation technology to obtain all verbs and the nouns immediately following them from the business requirements specification.

[0105] (2) Convert verbs into computable, structured vectors, such as Word2vce technology.

[0106] (3) Use the K-means clustering algorithm until the cluster centers no longer change.

[0107] (4) Compare the core words obtained from clustering with the target words to find the closest keywords.

[0108] The following section presents a combined application example of the inner-circle algorithm and the outer-circle algorithm.

[0109] (1) Outer ring analysis

[0110] The "Verb + Noun" extraction module: After a new "Business Requirements Specification" is generated and added to the database, the keyword extraction module uses word segmentation technology to automatically extract the verbs and their following nouns from the "Business Requirements Specification".

[0111] The module for calculating verb word vectors: uses Word2vce to calculate the word vectors of each verb.

[0112] Determine the K value module: Based on the professional background of the person who submitted the "Business Requirements Specification", check the business areas related to that professional background in the business architecture, and then find the number N of basic keywords in these business areas.

[0113] Run the k-means clustering module: Perform clustering using the k-means clustering algorithm. Use N as the number of clusters and run the algorithm until the cluster centers no longer change.

[0114] The module for generating words to be classified saves the clustering results as words to be classified separately.

[0115] (2) Inner Circle Analysis

[0116] Keyword benchmarking: Using the results of the K-means clustering algorithm as the analysis target, find similar verb keywords in the keyword database.

[0117] The business architecture and business model mapping module combines the nouns following the clustered verbs with verbs in the keyword library in the form of "verb + noun" to find the related business architecture and business model.

[0118] The relationship between each combination and the business architecture and business model is always one-to-many. Record the number of times each business architecture and business model is mapped.

[0119] (3) Results Display

[0120] Business architecture and business model sorting module: Sort business architectures and business models from highest to lowest frequency of occurrence. When multiple business models appear the same number of times, sort them from highest to lowest frequency of occurrence based on the closeness of the keywords mapping to that business model.

[0121] The module for displaying related business architecture and business models displays the analysis results on the screen.

[0122] Based on the above-described business architecture positioning method, this disclosure also provides a business architecture positioning device 10. The following will be combined with... Figure 10 The positioning device 10 of the business architecture is described in detail.

[0123] like Figure 10 As shown, the positioning device 10 of the business architecture includes a word segmentation module 1, a determination module 2, a positioning module 3, and an application module 4.

[0124] Word segmentation module 1 is used to perform operation S210: perform word segmentation on the obtained business requirement information to obtain m core words in the business requirement information, where m is an integer greater than or equal to 1.

[0125] Determine module 2, which is used to perform operation S220: match m core words with a pre-built keyword library to determine k keywords corresponding to the m core words. The keyword library includes n keywords, f business architectures and the mapping relationship between keywords and business architectures. k, n and f are all integers greater than or equal to 1, and k is less than n and n is greater than or equal to f.

[0126] Positioning module 3 is used to perform operation S230: locate the business architecture associated with k keywords corresponding to m core words according to the mapping relationship.

[0127] Application module 4 is used to perform operation S240: apply the associated business architecture as the business architecture of the business requirements.

[0128] According to some embodiments of this disclosure, the determining module may include a determining unit and a first calculation unit.

[0129] The determination unit is used to perform k-means clustering on m core words according to the pre-set number of clusters k, and obtain k matching words.

[0130] The first calculation unit is used to calculate the similarity between each matching word and each of the n keywords in the keyword library using a similarity calculation method, and takes the keyword corresponding to the maximum similarity value as the keyword corresponding to the matching word.

[0131] According to some embodiments of this disclosure, the positioning module may include a positioning unit, a second calculation unit, and a sorting unit.

[0132] The positioning unit is used to locate x pre-selected business architectures for business requirements by using the mapping relationship of each keyword among k keywords, where x is an integer greater than or equal to 1.

[0133] The second calculation unit is used to calculate the degree of correlation between each pre-selected business architecture and business requirements. The degree of correlation is calculated based on the closeness between keywords with mapping relationships and business architectures.

[0134] The sorting unit is used to rank the business architectures from highest to lowest degree of association, and the pre-selected business architecture corresponding to the highest degree of association is taken as the associated business architecture.

[0135] According to some embodiments of this disclosure, the positioning unit may include positioning elements and merging elements.

[0136] The positioning element is used to locate the g of a keyword by utilizing the mapping relationship between each keyword among k keywords. i A pre-selected business architecture, of which g i Let i be an integer greater than or equal to 1, and let i be an integer greater than or equal to 1 and less than or equal to k.

[0137] Merging elements are used to combine g1+g2+g3+......+g i +......+g k If the same business architecture is among the pre-selected business architectures, it will be merged into one, resulting in x pre-selected business architectures.

[0138] According to some embodiments of this disclosure, the second computing unit may include a first computing element and a second computing element.

[0139] The first computing element is used to determine the degree of correlation between the pre-selected business architecture and the business requirements when the pre-selected business architecture is the merged pre-selected business architecture. This correlation is the sum of the closeness between all pre-selected business architectures before the merger and their corresponding keywords.

[0140] The second computing element is used to determine the degree of correlation between the pre-selected business architecture and business requirements when the pre-selected business architecture is an unmerged pre-selected business architecture. This correlation is defined as the closeness between the pre-selected business architecture and the corresponding keywords.

[0141] According to some embodiments of this disclosure, a pre-built module for pre-constructing a keyword library may include a word segmentation unit and a storage unit.

[0142] The word segmentation unit is used to segment the architecture information of f business architectures to obtain n keywords. The architecture information includes architecture name, architecture definition and architecture rules.

[0143] The storage unit is used to store n keywords, f business architectures, and the mapping relationship between keywords and business architectures into the keyword library. The mapping relationship has a proximity attribute, which is the proximity between the keywords with the mapping relationship and the business architecture.

[0144] Since the positioning device of the above business architecture is set up based on the positioning method of the business architecture, the beneficial effects of the positioning device of the above business architecture are the same as those of the positioning method of the business architecture, and will not be repeated here.

[0145] Furthermore, according to embodiments of this disclosure, any multiple modules among the word segmentation module 1, the determination module 2, the positioning module 3, and the application module 4 can be merged into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module.

[0146] According to embodiments of this disclosure, at least one of the word segmentation module 1, determination module 2, positioning module 3, and application module 4 can be at least partially implemented as hardware circuitry, 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-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three methods of software, hardware, and firmware, or in a suitable combination of any of these.

[0147] Alternatively, at least one of the word segmentation module 1, the determination module 2, the positioning module 3, and the application module 4 can be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0148] Figure 11 A block diagram schematically illustrates an electronic device suitable for implementing the above-described method according to an embodiment of the present disclosure.

[0149] like Figure 11As shown, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 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.

[0150] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0151] According to embodiments of this disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0152] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0153] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0154] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the methods of the embodiments of this disclosure.

[0155] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0156] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0157] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0158] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can 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, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0159] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0160] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0161] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for locating business architecture, characterized in that, include: The acquired business requirement information is segmented into words to obtain m core words in the business requirement information, where m is an integer greater than or equal to 1. The m core words are matched with a pre-built keyword library to determine k keywords corresponding to the m core words. The keyword library includes n keywords, f business architectures and the mapping relationship between keywords and business architectures. k, n and f are all integers greater than or equal to 1, and k is less than n and n is greater than or equal to f. Based on the mapping relationship, locate the business architecture associated with the k keywords corresponding to the m core words; and The associated business architecture is applied as the business architecture for the business requirements. The pre-built keyword library includes: The obtained architecture information of f business architectures is segmented to obtain n keywords, where the architecture information includes architecture name, architecture definition, and architecture rules; and The n keywords, f business architectures, and the mapping relationships between keywords and business architectures are stored in the keyword library. The mapping relationships have a proximity attribute, which represents the proximity between the keywords with the mapping relationship and the business architecture. The method for determining the affinity between the keywords with mapping relationships and the business architecture includes: The affinity between the keywords obtained by segmenting the architecture name and the corresponding business architecture is set as a, the affinity between the keywords obtained by segmenting the architecture definition and the corresponding business architecture is set as b, and the affinity between the keywords obtained by segmenting the architecture rules and the corresponding business architecture is set as c, where 0 < a < 1, 0 < b < 1, 0 < c < 1, a > b > c.

2. The business architecture positioning method according to claim 1, characterized in that, The step of matching the core words with a pre-built keyword library to determine k keywords corresponding to the m core words includes: Based on a pre-defined number of clusters k, k-means clustering is performed on the m core words to obtain k matching words; and The similarity between each matching word and each of the n keywords in the keyword database is calculated using a similarity calculation method, and the keyword corresponding to the maximum similarity value is taken as the keyword corresponding to the matching word.

3. The business architecture positioning method according to claim 2, characterized in that, The business architecture for locating the association between the k keywords corresponding to the m core words based on the mapping relationship includes: Using the mapping relationship of each of the k keywords, x pre-selected business architectures for the business requirement are located, where x is an integer greater than or equal to 1; Calculate the degree of association between each of the pre-selected business architectures and the business requirements, wherein the degree of association is calculated based on the closeness between keywords with mapping relationships and the business architectures; and The pre-selected business architecture corresponding to the highest degree of correlation is selected as the business architecture associated with the correlation, based on the ranking from highest to lowest correlation.

4. The business architecture positioning method according to claim 3, characterized in that, The step of locating x pre-selected business architectures for the business requirement using the mapping relationship of each of the k keywords includes: Using the mapping relationship of each of the k keywords, locate the g of the keyword. i A pre-selected business architecture, of which g i Let be an integer greater than or equal to 1, and let i be an integer greater than or equal to 1 and less than or equal to k; and Add g1+g2+g3+……+g i +……+g k If the same business architecture is among the pre-selected business architectures, it will be merged into one, resulting in x pre-selected business architectures.

5. The business architecture positioning method according to claim 4, characterized in that, The calculation of the correlation between each of the pre-selected business architectures and the business requirements includes: When the pre-selected business architecture is the merged pre-selected business architecture, the degree of correlation between the pre-selected business architecture and the business requirement is the sum of the affinity between all pre-selected business architectures before the merger and their corresponding keywords; and When the pre-selected business architecture is an unmerged pre-selected business architecture, the degree of correlation between the pre-selected business architecture and the business requirement is the closeness between the pre-selected business architecture and the corresponding keyword.

6. A positioning device for a business architecture, characterized in that, include: The word segmentation module is used to perform word segmentation on the acquired business requirement information to obtain m core words in the business requirement information, where m is an integer greater than or equal to 1. The determining module is used to perform matching of the m core words with a pre-built keyword library to determine k keywords corresponding to the m core words. The keyword library includes n keywords, f business architectures and mapping relationships between keywords and business architectures, where k, n and f are all integers greater than or equal to 1, and k is less than n and n is greater than or equal to f. The positioning module is used to execute a business architecture that locates the k keywords associated with the m core words according to the mapping relationship; and The application module is used to execute the application of the associated business architecture as the business architecture for the business requirements. The pre-built keyword library includes: The obtained architecture information of f business architectures is segmented to obtain n keywords, where the architecture information includes architecture name, architecture definition, and architecture rules; and The n keywords, f business architectures, and the mapping relationships between keywords and business architectures are stored in the keyword library. The mapping relationships have a proximity attribute, which represents the proximity between the keywords with the mapping relationship and the business architecture. The method for determining the affinity between the keywords with mapping relationships and the business architecture includes: The affinity between the keywords obtained by segmenting the architecture name and the corresponding business architecture is set as a, the affinity between the keywords obtained by segmenting the architecture definition and the corresponding business architecture is set as b, and the affinity between the keywords obtained by segmenting the architecture rules and the corresponding business architecture is set as c, where 0 < a < 1, 0 < b < 1, 0 < c < 1, a > b > c.

7. An electronic device, characterized in that, include: One or more processors; One or more memories are provided for storing executable instructions that, when executed by the processor, implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The storage medium stores executable instructions that, when executed by a processor, implement the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that, The method includes a computer program comprising one or more executable instructions that, when executed by a processor, implement the method according to any one of claims 1 to 5.

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