CN116306630A

CN116306630A - Positioning method, device, electronic equipment, medium and program product of business architecture

Info

Publication number: CN116306630A
Application number: CN202310150453.1A
Authority: CN
Inventors: 陈璐璐; 杨婕; 秦瑶; 李森鹤
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2023-02-13
Filing date: 2023-02-13
Publication date: 2023-06-23

Abstract

The present disclosure provides a method, apparatus, electronic device, medium and computer program product for locating a business architecture. The method and the device can be used in the technical field of artificial intelligence. The positioning method of the service architecture comprises the following steps: performing word segmentation operation on the acquired service demand information of the service demand to obtain m core words in the service demand information, wherein m is an integer greater than or equal to 1; matching m core words with a pre-constructed keyword library, and determining k keywords corresponding to the m core words, wherein the keyword library comprises n keywords, f business frameworks and mapping relations between the keywords and the business frameworks, k, n and f are integers which are more than or equal to 1, k is less than n, and n is more than or equal to f; positioning business architecture associated with k keywords corresponding to m core words according to the mapping relation; and applying the associated business architecture as the business architecture of the business requirement.

Description

Positioning method, device, electronic equipment, medium and program product of business architecture

Technical Field

The present disclosure relates to the field of artificial intelligence technology, and more particularly, to a method, an apparatus, an electronic device, a medium, and a computer program product for locating a business architecture.

Background

As business architecture plays a greater role in the business development process of enterprises, the content of the business architecture becomes more and more complex. How to accurately locate the required business architecture when the business is innovated, and further, applying the required business architecture becomes more and more a core element for reducing maintenance of the business architecture. When the service innovation can accurately position the service architecture and is applied to the service innovation, the maintenance of the service architecture is completed through the service innovation, so that the service architecture is the most effective way for improving the service architecture value. The accurate positioning of the service architecture according to the requirements of service innovation becomes a critical bottleneck problem to be solved.

Disclosure of Invention

In view of this, the present disclosure provides a service architecture positioning method, apparatus, electronic device, computer-readable storage medium, and computer program product that are easy to implement and that can precisely position a desired service architecture.

One aspect of the present disclosure provides a method for locating a service architecture, including: performing word segmentation operation on the acquired business demand information of the business demand to obtain m core words in the business demand information, wherein m is an integer greater than or equal to 1; matching the m core words with a pre-constructed keyword library, and determining k keywords corresponding to the m core words, wherein the keyword library comprises n keywords, f business frameworks and mapping relations between the keywords and the business frameworks, wherein k, n and f are integers which are more than or equal to 1, k is less than n, and n is more than or equal to f; positioning business architecture associated with k keywords corresponding to the m core words according to the mapping relation; and applying the associated business architecture as the business architecture of the business requirement.

According to the positioning method of the business architecture according to the embodiment of the disclosure, based on m core words in the business requirement information, k keywords corresponding to the m core words can be determined by matching the m core words with a pre-constructed keyword library, and because the keyword library comprises n keywords, f business architectures and mapping relations between the keywords and the business architecture, the business architecture associated with the k keywords corresponding to the m core words can be positioned according to the mapping relations, and the associated business architecture can be further used as the business architecture of the business requirement. The positioning method solves the problem that enterprises cannot accurately position the required business architecture during business innovation, and is easy to realize and good in positioning accuracy.

In some embodiments, the matching the core word with a pre-constructed keyword library, determining k keywords corresponding to the m core words includes: according to the preset clustering number k, carrying out k-means clustering on the m core words to obtain k matched words; and calculating the similarity between each matching word and each keyword in n keywords in the keyword library by using a similarity calculation method, and taking the keyword corresponding to the maximum similarity value as the keyword corresponding to the matching word.

In some embodiments, the locating the business architecture associated with k keywords corresponding to the m core words according to the mapping relationship includes: positioning x pre-selected service frameworks of the service requirement by using the mapping relation of each keyword in the k keywords, wherein x is an integer greater than or equal to 1; calculating the association degree of each pre-selected service architecture and the service requirement, wherein the association degree is calculated according to the intimacy between the keywords with the mapping relation and the service architecture; and ranking from big to small according to the association degree, and taking a preselected service architecture corresponding to the association degree with the first ranking as the associated service architecture.

In some embodiments, the locating the x pre-selected business frameworks of the business requirement using the mapping relationship of each of the k keywords includes: locating g of each keyword by using the mapping relation of each keyword in the k keywords _i A pre-selected business architecture, wherein g _i Is an integer of 1 or more, i is an integer of 1 or more and k or lessThe method comprises the steps of carrying out a first treatment on the surface of the Will g ₁ +g ₂ +g ₃ +......+g _i +......+g _k The same service architecture in the pre-selected service architectures is combined into one to obtain x pre-selected service architectures.

In some embodiments, said calculating the degree of association of each of said preselected business frameworks with said business requirements comprises: when the pre-selected business architecture is the pre-selected business architecture after combination, the association degree of the pre-selected business architecture and the business requirement is the sum of affinities between all pre-selected business architectures before combination and the corresponding keywords; and when the pre-selected business architecture is an uncombined pre-selected business architecture, the association degree of the pre-selected business architecture and the business requirement is the intimacy between the pre-selected business architecture and the corresponding keywords.

In some embodiments, the pre-building keyword library includes: performing word segmentation operation on the obtained architecture information of the f business architectures to obtain n keywords, wherein the architecture information comprises architecture names, architecture definitions and architecture rules; and storing the n keywords, the f business frameworks and the mapping relation between the keywords and the business frameworks to the keyword library, wherein the mapping relation has a affinity attribute, and the affinity attribute is the affinity between the keywords with the mapping relation and the business frameworks.

In some embodiments, the method for determining the affinity between the keyword with the mapping relationship and the business architecture includes: the method comprises the steps of setting the intimacy between a keyword obtained by word segmentation operation on the architecture name and a corresponding business architecture as a, setting the intimacy between a keyword obtained by word segmentation operation on the architecture definition and a corresponding business architecture as b, and setting the intimacy between a keyword obtained by word segmentation operation on the architecture rule and a corresponding business architecture as c, wherein 0 < a < 1,0 < b < 1,0 < c < 1, and a > b > c.

Another aspect of the present disclosure provides a positioning device of a service architecture, including: the word segmentation module is used for executing word segmentation operation on the acquired business requirement information of the business requirement to obtain m core words in the business requirement information, wherein m is an integer greater than or equal to 1; the determining module is used for matching the m core words with a pre-constructed keyword library and determining k keywords corresponding to the m core words, wherein the keyword library comprises n keywords, f business frameworks and mapping relations between the keywords and the business frameworks, k, n and f are integers which are more than or equal to 1, k is less than n, and n is more than or equal to f; the positioning module is used for executing a business architecture associated with k keywords corresponding to the m core words according to the mapping relation; and the application module is used for executing application of the associated business architecture as the business architecture of the business requirement.

Another aspect of the present disclosure provides an electronic device comprising one or more processors and one or more memories, wherein the memories are configured to store executable instructions that, when executed by the processors, implement the method as described above.

Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions that, when executed, are configured to implement a method as described above.

Another aspect of the present disclosure provides a computer program product comprising a computer program comprising computer executable instructions which, when executed, are for implementing a method as described above.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments thereof with reference to the accompanying drawings in which:

FIG. 1 schematically illustrates an exemplary system architecture to which methods, apparatuses may be applied according to embodiments of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a method of locating a business architecture according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow diagram of pre-building keyword libraries according to an embodiment of the present disclosure;

FIG. 4 schematically illustrates a flow chart for matching a core word with a pre-constructed keyword library to determine k keywords corresponding to m core words, according to an embodiment of the present disclosure;

FIG. 5 schematically illustrates a flow diagram of a business architecture for locating k keyword associations corresponding to m core words according to a mapping relationship, according to an embodiment of the present disclosure;

FIG. 6 schematically illustrates a flow chart of x preselected business frameworks that locate business needs using the mapping relationship of each of the k keywords according to an embodiment of the present disclosure;

FIG. 7 schematically illustrates a flow chart for calculating the degree of association of each preselected business architecture with business needs in accordance with an embodiment of the present disclosure;

FIG. 8 schematically illustrates a correspondence diagram of matching words to a preselected business architecture according to an embodiment of the present disclosure;

FIG. 9 schematically illustrates an algorithm nesting schematic according to embodiments of the present disclosure;

FIG. 10 schematically illustrates a block diagram of a positioning device of a business architecture according to an embodiment of the disclosure;

fig. 11 schematically illustrates a block diagram of an electronic device according to an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related personal information of the user all conform to the regulations of related laws and regulations, necessary security measures are taken, and the public order harmony is not violated. In the technical scheme of the disclosure, the processes of acquiring, collecting, storing, using, processing, transmitting, providing, disclosing, applying and the like of the data all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public order harmony is not violated.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

Where a formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features.

Embodiments of the present disclosure provide a method, apparatus, electronic device, computer-readable storage medium, and computer program product for locating a business architecture. The positioning method of the service architecture comprises the following steps: performing word segmentation operation on the acquired service demand information of the service demand to obtain m core words in the service demand information, wherein m is an integer greater than or equal to 1; matching m core words with a pre-constructed keyword library, and determining k keywords corresponding to the m core words, wherein the keyword library comprises n keywords, f business frameworks and mapping relations between the keywords and the business frameworks, k, n and f are integers which are more than or equal to 1, k is less than n, and n is more than or equal to f; positioning business architecture associated with k keywords corresponding to m core words according to the mapping relation; and applying the associated business architecture as the business architecture of the business requirement.

It should be noted that, the positioning method, apparatus, electronic device, computer readable storage medium and computer program product of the business architecture of the present disclosure may be used in the field of artificial intelligence technology, and may also be used in any field other than the field of artificial intelligence technology, such as the financial field, where the field of the present disclosure is not limited.

Fig. 1 schematically illustrates an exemplary system architecture 100 of a positioning method, apparatus, electronic device, computer-readable storage medium and computer program product, where a business architecture may be applied, according to embodiments of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios.

As shown in fig. 1, a system architecture 100 according to this embodiment may include

terminal devices

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

terminal devices

101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the

terminal devices

101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the

terminal devices

101, 102, 103.

The

terminal devices

101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

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

terminal devices

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

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

terminal devices

101, 102, 103 and/or the server 105. Accordingly, the positioning device of the service architecture provided by the embodiments of the present disclosure may also be provided in a server or a server cluster different from the server 105 and capable of communicating with the

terminal devices

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

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

The following describes in detail the positioning method of the service architecture according to the embodiment of the present disclosure through fig. 2 to 8 based on the scenario described in fig. 1.

Fig. 2 schematically illustrates a flow chart of a method of locating a business architecture according to an embodiment of the disclosure.

As shown in fig. 2, the positioning method of the service architecture of this embodiment includes operations S210 to S240.

In operation S210, word segmentation is performed on the acquired service requirement information of the service requirement to obtain m core words in the service requirement information, where m is an integer greater than or equal to 1. For example, the word segmentation technology can be used for operating the business requirement information, and m core words can be obtained.

In operation S220, the m core words are matched with a pre-constructed keyword library, and k keywords corresponding to the m core words are determined, wherein the keyword library includes n keywords, f service frameworks, and a mapping relationship between the keywords and the service frameworks, k, n, and f are integers greater than or equal to 1, k is less than n, and n is greater than or equal to f.

As one possible implementation, as shown in fig. 3, pre-building a keyword library includes operation S310 and operation S320.

In operation S310, the obtained architecture information of the f service architectures is subjected to word segmentation operation, so as to obtain n keywords, where the architecture information includes architecture names, architecture definitions and architecture rules. For example, the word segmentation technique may be used to operate on the architecture name, architecture definition, and architecture rule of each of the f service architectures, so as to obtain n keywords. The f service frameworks can be understood herein as names of the f service frameworks.

In operation S320, n keywords, f business frameworks, and mapping relations between the keywords and the business frameworks are stored in the keyword library, where the mapping relation has a affinity attribute, and the affinity attribute is an affinity between the keywords having the mapping relation and the business frameworks.

Operations S310 and S320 are described below using specific service architecture examples, such as a service architecture for in-house deposit, which is named in-house deposit; the architecture is defined as performing deposit transaction and deposit management on users who open accounts within the environment; the architecture rules are as follows: rule one: the original information of the identity card is needed when the deposit is transacted, a deposit form is filled in, and the filling information of the deposit form user comprises the name of the user, the address of the user, the occupation of the user, the deposit amount and the like; rule II: the deposit amount is less than 3% in 50-100 case years, 4% in 50-100 case years, 5% in more than 100 case years, etc.

The business architecture of the deposit in the environment is segmented by using the segmentation technology, namely, deposit handling is carried out on the deposit of the user who opens an account in the environment, and the deposit management is carried out on the rule one: the original information of the identity card is needed when the deposit is transacted, a deposit form is filled in, and the filling information of the deposit form user comprises the name of the user, the address of the user, the occupation of the user, the deposit amount and the like; rule II: the keywords "within", "deposit", "account opening", "user", "deposit handling", "deposit management", "identity card original", "fill-in deposit bill", "fill-in information of single user", "user name", "user address", "user occupation", "deposit amount", "less than", "50 ten thousand", "one year", "interest rate of 3%", "50 ten thousand-100 ten thousand", "interest rate of 4%", "greater than 100 ten thousand", and "interest rate of 5%", can be obtained by dividing the words "the interest rate of less than 50 ten thousand-year, the interest rate of 50 ten thousand-100 ten thousand", "the interest rate of greater than 100 ten thousand", etc.

Thus, keywords "in", "deposit", "account opening", "user", "deposit transaction", "deposit management", "original identity card", "fill-in deposit sheet", "deposit sheet user fill-in information", "user name", "user address", "user occupation", "deposit amount", "less than", "50 ten thousand", "one year", "interest rate 3%", "50 ten thousand-100 ten thousand", "interest rate 4%", "greater than 100 ten thousand" and "interest rate 5%", can be stored in the keyword library, and the business architecture of the in-deposit can be stored in the keyword library, and the keywords "in-deposit", "account opening", "user", "deposit transaction", "deposit management", "original identity card", "fill-in deposit sheet", "deposit sheet user fill-in information", "user name", "user address", "user occupation", "deposit amount", "less than", "50 ten thousand", "one year", "interest rate 3%", "50 ten thousand-100 ten thousand", "interest rate 4%", "greater than 100 ten thousand" and "interest rate 5%", and the business architecture of in-deposit can all have mapping relation, so that it is necessary to store the keywords in-deposit business architecture.

The above-mentioned business architecture of the in-deposit is merely illustrative, and not to be construed as limiting the present disclosure, and the f business architectures mentioned in the present disclosure may be any type of business architecture, and are not limited to the in-deposit business architecture. At least one keyword can be obtained by performing the above operation on a specific service architecture, and n keywords can be obtained by performing the above operation on each of the f service architectures. Wherein, there may be repeated keywords between different service architectures in the f service architectures, so there may be a mapping relationship between the same keyword and different service architectures, and the affinities between the keyword and different service architectures may be the same or different.

The pre-construction of the keyword library may be facilitated through operations S310 and S320.

It should be noted that, the mapping relationship has a affinity attribute, and the affinity attribute is the affinity between the keyword with the mapping relationship and the business architecture. The affinity between the keyword and the business architecture can be determined by an affinity rule, and the affinity rule can specifically include that the affinity between the keyword obtained by word segmentation in the architecture name and the business architecture is greater than that between the keyword obtained by word segmentation in the architecture definition and the business architecture, and the affinity between the keyword obtained by word segmentation in the architecture rule and the business architecture is greater than that between the keyword obtained by word segmentation in the architecture rule and the business architecture.

In some specific examples, a method for determining affinity between a keyword having a mapping relationship and a business architecture includes: the method comprises the steps of setting the intimacy between a keyword obtained by word segmentation operation on a framework name and a corresponding business framework as a, setting the intimacy between a keyword obtained by word segmentation operation on a framework definition and a corresponding business framework as b, and setting the intimacy between a keyword obtained by word segmentation operation on a framework rule and a corresponding business framework as c, wherein 0 < a < 1,0 < b < 1,0 < c < 1, and a > b > c. Therefore, the intimacy between n keywords obtained from the architecture information of the business architecture and the business architecture can be determined conveniently. The n keywords have affinity with the business architecture respectively, so that the subsequent operation S230 can locate the business architecture associated with k keywords corresponding to the m core words according to the mapping relationship more accurately.

In operation S230, the business architecture associated with k keywords corresponding to the m core words is located according to the mapping relationship.

In operation S240, the associated service architecture is applied as the service architecture of the service requirement.

Fig. 4 schematically illustrates a flowchart for matching a core word with a pre-constructed keyword library to determine k keywords corresponding to m core words, according to an embodiment of the present disclosure.

Operation S220 matches the core word with a keyword library constructed in advance, determines k keywords corresponding to m core words, and includes operation S221 and operation S222.

In operation S221, k-means clustering is performed on the m core words according to the preset number k of clusters, to obtain k matched words.

In operation S222, the similarity between each matching word and each keyword in the n keywords in the keyword library is calculated by using the similarity calculation method, and the keyword corresponding to the maximum similarity value is used as the keyword corresponding to the matching word.

It can be understood that when K-means clustering is performed on m core words, each core word needs to be converted into a word vector, so that the obtained K matching words can be the matching word vector, each keyword in n keywords in the keyword library can be converted into the word vector, and then similarity between each matching word and each keyword in n keywords in the keyword library can be calculated by using a similarity calculation method, such as a cosine similarity algorithm, so that n similarity values can be obtained, the largest similarity value can be found for ranking the n similarity values from large to small, and the keyword corresponding to the largest similarity value is used as the keyword corresponding to the matching word. Thus, matching the core words with the keyword library constructed in advance, k keywords corresponding to m core words are determined through operation S221 and operation S222 can be facilitated.

Fig. 5 schematically illustrates a flowchart of a business architecture for locating k keyword associations corresponding to m core words according to a mapping relationship according to an embodiment of the present disclosure.

Operation S230 locates, according to the mapping relationship, the business architecture associated with k keywords corresponding to the m core words, including operations S231 to S233.

In operation S231, x pre-selected service frameworks of the service requirement are located by using the mapping relation of each keyword in the k keywords, where x is an integer greater than or equal to 1.

As one possible implementation, as shown in fig. 6, operation S231 locates x pre-selected service frameworks of the service requirement using the mapping relationship of each of the k keywords, including operation S2311 and operation S2312.

In operation S2311, g of each of the k keywords is located using the mapping relationship of the keyword _i A pre-selected business architecture, wherein g _i I is an integer of 1 or more and k or less.

In operation S2312, g ₁ +g ₂ +g ₃ +......+g _i +......+g _k Personal preselection industryThe same business architecture in the business architecture is combined into one to obtain x pre-selected business architectures. Thus, x pre-selected service frameworks for locating the service requirement using the mapping relationship of each of the k keywords can be conveniently implemented through operations S2311 and S2312.

In operation S232, a degree of association between each pre-selected business architecture and the business requirement is calculated, wherein the degree of association is calculated according to the intimacy between the keywords with the mapping relationship and the business architecture.

As an implementation manner, as shown in fig. 7, operation S232 calculates a degree of association between each pre-selected service architecture and a service requirement, including operation S2321 and operation S2322.

In operation S2321, when the pre-selected service architecture is the post-merging pre-selected service architecture, the association degree of the pre-selected service architecture and the service requirement is the sum of affinities between all pre-selected service architectures before merging and the corresponding keywords.

In operation S2322, when the pre-selected service architecture is an uncombined pre-selected service architecture, the degree of association of the pre-selected service architecture with the service requirement is the affinity between the pre-selected service architecture and the corresponding keyword. Thus, calculating the degree of association of each preselected business architecture with business requirements may be facilitated through operations S2321 and S2322.

In operation S233, the pre-selected business architecture corresponding to the first association degree is ranked according to the association degree from large to small, and the pre-selected business architecture is used as the associated business architecture. Therefore, through operations S231 to S233, it is possible to facilitate the realization of locating the business architecture associated with k keywords corresponding to m core words according to the mapping relationship.

Operations S231 to S233 are exemplarily described below with reference to the correspondence between the matching words and the preselected service architecture in fig. 8, and fig. 8 is merely an illustration and is not to be construed as limiting the present disclosure.

The number k of clusters is assumed to be 3, so that 3 matching words, namely matching word A, matching word B and matching word C, can be obtained through k-means clustering. The keyword A corresponding to the matching word A, the keyword B corresponding to the matching word B and the keyword C corresponding to the matching word C can be found in the keyword library by utilizing the similarity calculation method.

In the keyword library, the business architecture having a mapping relation with the keyword A comprises a business architecture a, a business architecture b and a business architecture c, the intimacy between the keyword A and the business architecture a is W1, the intimacy between the keyword A and the business architecture b is W2, and the intimacy between the keyword A and the business architecture c is W3. The business architecture with the mapping relation with the keyword B comprises a business architecture a and a business architecture d, the intimacy between the keyword B and the business architecture a is W4, and the intimacy between the keyword B and the business architecture d is W5. The business architecture with the mapping relation with the keyword C comprises a business architecture a, a business architecture C, a business architecture d and a business architecture e, the intimacy between the keyword C and the business architecture a is W6, the intimacy between the keyword C and the business architecture C is W7, the intimacy between the keyword C and the business architecture d is W8, and the intimacy between the keyword C and the business architecture e is W9.

Therefore, 5 pre-selected service frameworks of the service requirement, namely a service framework a, a service framework B, a service framework C, a service framework d and a service framework e, can be positioned by utilizing the mapping relation of the keyword A, the keyword B and the keyword C.

Specifically, 3 pre-selected service frameworks, namely service framework a, service framework b and service framework c, can be located by using the keyword a. 2 pre-selected business frameworks, business framework a and business framework d, can be located by utilizing the keyword B. 4 pre-selected service frameworks, namely a service framework a, a service framework C, a service framework d and a service framework e, can be positioned by utilizing the keyword C. In the plurality of pre-business frameworks positioned by the key word A, the key word B and the key word C, the business frameworks a, the business frameworks C and the business frameworks d are all repeated business frameworks, so that 3 business frameworks a are combined into one business framework a to serve as 1 pre-business framework, 2 business frameworks C are combined into one business framework C to serve as 1 pre-business framework, 2 business frameworks d are combined into one business framework d to serve as 1 pre-business framework, and 5 pre-selected business frameworks corresponding to business requirements are obtained, namely, the business frameworks a, the business framework B, the business framework C, the business framework d and the business framework e.

Because the pre-selected business architecture a is a business architecture after combination, the association degree of the pre-selected business architecture a and the business requirement is the sum of the affinity W1 between the pre-selected business architecture a and the keyword A, the affinity W4 between the pre-selected business architecture a and the keyword B and the affinity W6 between the pre-selected business architecture a and the keyword C, namely the association degree of the pre-selected business architecture a and the business requirement is W1+W4+W6.

Since the pre-selected business architecture b is an uncombined business architecture, the association degree of the pre-selected business architecture b and the business requirement is the intimacy W2 between the pre-selected business architecture b and the keyword a.

Similarly, the association degree between the preselected service architecture c, the preselected service architecture d and the preselected service architecture e and the service requirement can be obtained, and the description is omitted here.

And ranking from big to small according to the association degree, namely taking the preselected business architecture corresponding to the association degree with the first ranking as the associated business architecture.

The following describes in detail a positioning method of a service architecture according to an embodiment of the present disclosure with reference to fig. 9. It is to be understood that the following description is exemplary only and is not intended to limit the disclosure in any way.

According to the positioning method of the business architecture of the embodiment of the disclosure, two different algorithms are combined, one is to form a keyword library by taking the business architecture and the business model as cores, and the other is to analyze the relation between the business requirement specification and the business architecture and the business model by using a K-means clustering algorithm from the business requirement specification to be analyzed. The combination not only solves the problem of service architecture analysis on innovative services, but also improves the accuracy of service architecture analysis

As shown in fig. 9, the present disclosure provides a more accurate analysis by algorithmic nesting. Algorithm nesting includes an inner ring algorithm and an outer ring algorithm. The inner ring algorithm and the outer ring algorithm are described in detail below.

Inner circle algorithm: and constructing a keyword library, and improving accuracy by using an affinity method.

(1) The system automatically uses an affinity method to construct a basic keyword library: based on the content of the business architecture and the business model, core words are found out by using word segmentation technology: the model core word related to the flow consists of verbs and nouns, and the verbs and nouns are respectively formed into a verb group and a noun group; the model related to things is made up of "nouns".

And ordering the obtained core words according to the intimacy degree by using a intimacy degree method based on names, definitions and rules of the service architecture and the service model, setting parameter values by using expert experience, and screening available keywords. The keywords form a basic keyword library according to verbs and nouns respectively, and an association relation between the keywords and a business architecture or a business model is established.

(2) Expanding keyword libraries

Based on the keywords in the basic keyword library, the synonyms and the paraphraseology are supplemented to form an expanded keyword library. The expanded keywords still establish association relations according to the association of the basic keywords, the business architecture and the business model.

After the keyword library and the association with the business architecture and the business model are established, a plurality of keywords are often formed in the same business architecture or business model, but the affinities of different keywords and the architecture or model are different. The same keyword is often related to multiple business architectures or business models, but the same keyword also has a difference in affinity with different business architectures or business models.

(3) Keyword library usage

When a keyword is used for inquiring a service architecture related to the keyword, the system automatically displays the keyword in a ranking manner from high to low according to the intimacy; when a plurality of keywords are used for inquiring, the intimacy degree of the keywords and the business architecture or business model is calculated in a combined mode, and then the keywords are displayed in a descending order according to the intimacy degree.

For example, the use of a "deposit" keyword may be related to a plurality of business frameworks or business models, and together with the keywords of agreements, interest, and support, each keyword may map to a plurality of business frameworks or business models, but in combination, it may be explicitly directed to a specific business framework or business model. The combination of partial groups can also specify a certain business architecture and business model, such as deposit, agreement and interest; deposit, agreement and withdrawal; deposit, interest, withdrawal, etc. And then obtaining the most accurate service architecture and service model according to the most complete keyword combination, gradually reducing keywords to obtain the matched service architecture and service model, calculating the intimacy to obtain the priority.

Outer ring algorithm: and (5) expanding by using a K-means clustering algorithm.

The number of keywords stored in the keyword library is relatively stable, and particularly, the content added is limited under the condition that the service architecture is not changed greatly. The innovation of the service is multiplied, especially the innovation of a complex service model, if keywords cannot be timely supplemented, the result of the service architecture analysis is inaccurate, and the service architecture is affected. Therefore, a K-means clustering algorithm taking the keywords as the core is established to expand the service range which can be covered and enhance the accuracy of service architecture analysis.

K mean value clustering algorithm:

target word: clustering is carried out by taking keywords in a keyword library as targets.

K value selection: and (3) providing departments according to the requirements, and finding out the number of corresponding basic keywords according to the associated business architecture, wherein the number is used as the clustering number K.

The object under analysis: and a natural language analysis algorithm is used for finding out core words for the service description part in the service requirement description for service architecture analysis.

Calculation logic:

(1) Obtaining all verbs and subsequent nouns from business requirement specifications using word segmentation techniques

(2) The verbs are converted into computable, structured vectors, such as Word2vce technology.

(3) Using K-means clustering algorithm until no change occurs in the clustering center

(4) And comparing the core words obtained by clustering with the target words, and finding out the closest key words.

An example of the combined application of the inner-ring algorithm and the outer-ring algorithm is described below.

(1) Outer circle analysis

Extracting a verb+noun module: after a new service demand instruction is generated and put in storage in the system, a keyword extraction module automatically extracts verbs and subsequent nouns from the service demand instruction by using a word segmentation technology.

Word vector module for calculating verbs: word2vce is used to calculate the Word vector of each verb.

And a K value determining module: according to the professional of the personnel submitting the service requirement specification, the service field relevant to the professional in the service architecture is checked, and then the number N of basic keywords in the service fields is searched.

And (3) running a k-means clustering module: clustering is carried out by using a K-means clustering algorithm. N is used as the number of clusters, and the algorithm is operated until the cluster center is not changed any more.

Generating a word module to be classified: and independently storing the clustering result as the word to be classified.

(2) Inner circle analysis

The keyword library of the label: and (3) taking a result obtained by the K-means clustering algorithm as an analysis target, and finding out verb keywords similar to the K-means clustering algorithm from a keyword library.

Mapping service architecture and service model module: combining the verbs after the clustered verbs with verbs in a keyword library in a verb+noun mode to find out a related business architecture and business model.

The relationship of each combination to the business architecture and business model must be a one-to-many relationship. The number of times each business architecture and business model is mapped is recorded.

(3) Results display

Service architecture and service model ordering module: and sequencing the service architecture and the service model from high to low according to the occurrence times. When the times of the occurrence of the plurality of business models are the same, the intimacy of the keywords mapped to the business models and the models is ranked from high to low.

And displaying the associated service architecture and the service model module: and displaying the analysis result on a display screen.

Based on the above-mentioned positioning method of the service architecture, the present disclosure further provides a positioning device 10 of the service architecture. The positioning device 10 of the business architecture will be described in detail below with reference to fig. 10.

As shown in fig. 10, 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.

The word segmentation module 1, the word segmentation module 1 is configured to perform operation S210: and performing word segmentation operation on the acquired service requirement information of the service requirement to obtain m core words in the service requirement information, wherein m is an integer greater than or equal to 1.

A determining module 2, the determining module 2 is configured to perform operation S220: and matching the m core words with a pre-constructed keyword library, and determining k keywords corresponding to the m core words, wherein the keyword library comprises n keywords, f business frameworks and mapping relations between the keywords and the business frameworks, wherein k, n and f are integers which are more than or equal to 1, k is less than n, and n is more than or equal to f.

Positioning module 3, positioning module 3 is configured to perform operation S230: and positioning the business architecture associated with k keywords corresponding to the m core words according to the mapping relation.

The application module 4, the application module 4 is configured to perform operation S240: and applying the associated service architecture as the service architecture of the service requirement.

According to some embodiments of the present disclosure, the determining module may include a determining unit and a first calculating unit.

The determining unit is used for carrying out k-means clustering on the m core words according to the preset clustering number k to obtain k matched words.

The first calculation unit is used for calculating the similarity between each matching word and each keyword in n keywords in the keyword library by using a similarity calculation method, and taking the keyword corresponding to the maximum similarity value as the keyword corresponding to the matching word.

According to some embodiments of the present disclosure, the positioning module may include a positioning unit, a second computing unit, and a ranking unit.

The positioning unit is used for positioning x pre-selected service frameworks of the service requirement by using the mapping relation of each keyword in the k keywords, wherein x is an integer greater than or equal to 1.

The second calculation unit is used for calculating the association degree of each pre-selected business architecture and business requirements, wherein the association degree is calculated according to the intimacy between the keywords with the mapping relation and the business architecture.

The sorting unit is used for ranking from big to small according to the association degree, and taking the preselected business architecture corresponding to the association degree with the first ranking as the associated business architecture.

According to some embodiments of the present disclosure, the positioning unit may comprise a positioning element and a merging element.

A positioning element for positioning g of each of the k keywords by using the mapping relation of the keyword _i A pre-selected business architecture, wherein g _i I is an integer of 1 or more and k or less.

Merging element for merging g ₁ +g ₂ +g ₃ +......+g _i +......+g _k The same service architecture in the pre-selected service architectures is combined into one to obtain x pre-selected service architectures.

According to some embodiments of the present disclosure, the second computing unit may include a first computing element and a second computing element.

The system comprises a first computing element, a second computing element and a third computing element, wherein the first computing element is used for when the pre-selected business architecture is the pre-selected business architecture after combination, the association degree of the pre-selected business architecture and business requirements is the sum of affinities between all pre-selected business architectures before combination and corresponding keywords.

And the second computing element is used for, when the pre-selected business architecture is an uncombined pre-selected business architecture, enabling the association degree of the pre-selected business architecture and the business requirement to be the intimacy between the pre-selected business architecture and the corresponding keywords.

According to some embodiments of the present disclosure, a pre-construction module for pre-constructing a keyword library may include a word segmentation unit and a storage unit.

The word segmentation unit is used for carrying out word segmentation operation on the obtained architecture information of the f business architectures to obtain n keywords, wherein the architecture information comprises architecture names, architecture definitions and architecture rules.

The storage unit is used for storing n keywords, f business frameworks and mapping relations between the keywords and the business frameworks to the keyword library, wherein the mapping relations have affinity attributes, and the affinity attributes are affinities between the keywords with the mapping relations and the business frameworks.

Since the positioning device of the service architecture is set based on the positioning method of the service architecture, the beneficial effects of the positioning device of the service architecture are the same as those of the positioning method of the service architecture, and are not repeated here.

In addition, according to an embodiment of the present disclosure, any of the word segmentation module 1, the determination module 2, the positioning module 3, and the application module 4 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module.

According to embodiments of the present disclosure, at least one of the segmentation module 1, the determination module 2, the positioning module 3, and the application module 4 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or in hardware or firmware in any other reasonable way of integrating or packaging the circuits, or in any one of or a suitable combination of three of software, hardware, and firmware.

Alternatively, at least one of the word segmentation module 1, the determination module 2, the positioning module 3 and the application module 4 may be at least partially implemented as a computer program module, which, when executed, may perform the respective functions.

Fig. 11 schematically shows a block diagram of an electronic device adapted to implement the above-described method according to an embodiment of the present disclosure.

As shown in fig. 11, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901 that 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)), or the like. Processor 901 may also include on-board memory for caching purposes. Processor 901 may include a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the present disclosure.

In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. The processor 901 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 902 and/or the RAM 903. Note that the program may be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the disclosure, the electronic device 900 may also include an input/output (I/O) interface 905, the input/output (I/O) interface 905 also being connected to the 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, a mouse, and the like; an output portion 907 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 908 including a hard disk or the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to an input/output (I/O) interface 905 as needed. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 910 so that a computer program read out therefrom is installed into the storage section 908 as needed.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, 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.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to perform the methods of embodiments of the present disclosure.

The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed, and downloaded and installed in the form of a signal on a network medium, via communication portion 909, and/or installed from removable medium 911. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909 and/or installed from the removable medium 911. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in 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, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

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

Claims

1. A method for locating a service architecture, comprising:

performing word segmentation operation on the acquired business demand information of the business demand to obtain m core words in the business demand information, wherein m is an integer greater than or equal to 1;

Matching the m core words with a pre-constructed keyword library, and determining k keywords corresponding to the m core words, wherein the keyword library comprises n keywords, f business frameworks and mapping relations between the keywords and the business frameworks, wherein k, n and f are integers which are more than or equal to 1, k is less than n, and n is more than or equal to f;

positioning business architecture associated with k keywords corresponding to the m core words according to the mapping relation; and

and applying the associated service architecture as the service architecture of the service requirement.

2. The method for locating a business architecture according to claim 1, wherein said matching the core words with a pre-constructed keyword library to determine k keywords corresponding to the m core words comprises:

according to the preset clustering number k, carrying out k-means clustering on the m core words to obtain k matched words; and

and calculating the similarity of each matching word and each keyword in n keywords in the keyword library by using a similarity calculation method, and taking the keyword corresponding to the maximum similarity value as the keyword corresponding to the matching word.

3. The method for locating a service architecture according to claim 2, wherein locating a service architecture associated with k keywords corresponding to the m core words according to the mapping relationship includes:

Positioning x pre-selected service frameworks of the service requirement by using the mapping relation of each keyword in the k keywords, wherein x is an integer greater than or equal to 1;

calculating the association degree of each pre-selected service architecture and the service requirement, wherein the association degree is calculated according to the intimacy between the keywords with the mapping relation and the service architecture; and

and ranking according to the association degree from big to small, and taking a preselected service architecture corresponding to the association degree with the first ranking as the associated service architecture.

4. A method for locating a service architecture according to claim 3, wherein said locating x pre-selected service architectures of said service requirement using a mapping relationship of each of said k keywords comprises:

locating g of each keyword by using the mapping relation of each keyword in the k keywords _i A pre-selected business architecture, wherein g _i I is an integer of 1 or more and k or less; and

will g ₁ +g ₂ +g ₃ +......+g _i +......+g _k The same service architecture in the pre-selected service architectures is combined into one to obtain x pre-selected service architectures.

5. The method for locating a business architecture according to claim 4, wherein said calculating the degree of association of each of said preselected business architectures with said business requirements comprises:

When the pre-selected business architecture is the pre-selected business architecture after combination, the association degree of the pre-selected business architecture and the business requirement is the sum of affinities between all pre-selected business architectures before combination and the corresponding keywords; and

when the pre-selected business architecture is an uncombined pre-selected business architecture, the association degree of the pre-selected business architecture and the business requirement is the intimacy between the pre-selected business architecture and the corresponding keywords.

6. The method for locating a service architecture according to any one of claims 1 to 5, wherein the pre-building a keyword library includes:

performing word segmentation operation on the obtained architecture information of the f business architectures to obtain n keywords, wherein the architecture information comprises architecture names, architecture definitions and architecture rules; and

storing the n keywords, f business frameworks and the mapping relation between the keywords and the business frameworks to the keyword library, wherein the mapping relation has a affinity attribute, and the affinity attribute is the affinity between the keywords with the mapping relation and the business frameworks.

7. The method for locating a business architecture according to claim 6, wherein the method for determining the affinity between the keyword with the mapping relationship and the business architecture comprises the following steps:

The method comprises the steps of setting the intimacy between a keyword obtained by word segmentation operation on the architecture name and a corresponding business architecture as a, setting the intimacy between a keyword obtained by word segmentation operation on the architecture definition and a corresponding business architecture as b, and setting the intimacy between a keyword obtained by word segmentation operation on the architecture rule and a corresponding business architecture as c, wherein 0 < a < 1,0 < b < 1,0 < c < 1, and a > b > c.

8. A positioning device for a service architecture, comprising:

the word segmentation module is used for executing word segmentation operation on the acquired business requirement information of the business requirement to obtain m core words in the business requirement information, wherein m is an integer greater than or equal to 1;

the determining module is used for matching the m core words with a pre-constructed keyword library and determining k keywords corresponding to the m core words, wherein the keyword library comprises n keywords, f business frameworks and mapping relations between the keywords and the business frameworks, k, n and f are integers which are more than or equal to 1, k is less than n, and n is more than or equal to f;

The positioning module is used for executing a business architecture associated with k keywords corresponding to the m core words according to the mapping relation; and

and the application module is used for executing application by taking the associated business architecture as the business architecture of the business requirement.

9. An electronic device, comprising:

one or more processors;

one or more memories for storing executable instructions which, when executed by the processor, implement the method of any of claims 1-7.

10. A computer readable storage medium, characterized in that the storage medium has stored thereon executable instructions which, when executed by a processor, implement the method according to any of claims 1-7.

11. A computer program product comprising a computer program comprising one or more executable instructions which when executed by a processor implement the method according to any one of claims 1 to 7.