Modular product construction method, electronic equipment and computer readable storage medium

Abstract

The invention discloses a modular product construction method, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a scheme demand, and decomposing the scheme demand into a plurality of scene clusters; each scene cluster is matched with a function demand, and the scene cluster comprises all function points for realizing the function demand; clustering all the scene clusters based on the function points in all the scene clusters to obtain at least one common cluster set; each common cluster set comprises at least one scene cluster; obtaining a function point included in each scene cluster in the public cluster set as a public function point, and screening from the public cluster set based on the function points except the public function point to obtain a basic scene cluster; obtaining a product set matched with the basic scene cluster, and obtaining products capable of realizing the common function points from the product set for combination to obtain a product combination module; wherein the products in the product set can realize the function points in the basic scene cluster. According to the scheme, the universality of product combination can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, and particularly relates to a modular product construction method, an electronic device and a computer readable storage medium. BACKGROUND

[0002] With the development of modern enterprises, how to use the products of the enterprise to solve the problems of different needs has become an important part of data analysis. In the prior art, a customized product combination is usually provided based on the problems to be solved according to specific needs, which leads to the difficulty of the product combination to adapt to different needs and the inability to be called in a modular form, and the generalization is poor. Therefore, how to improve the generalization of the product combination has become a problem to be solved. SUMMARY

[0003] The technical problem solved by the present application is to provide a modular product construction method, an electronic device and a computer readable storage medium, which can improve the generalization of the product combination.

[0004] To solve the above technical problem, the first aspect of the present application provides a modular product construction method, comprising: acquiring a scheme requirement, and decomposing the scheme requirement into a plurality of scene clusters; wherein each scene cluster matches a function requirement, and the scene cluster includes all function points for realizing the function requirement; clustering all the scene clusters based on the function points in all the scene clusters to obtain at least one public cluster set; wherein each public cluster set includes at least one scene cluster; acquiring function points included in each scene cluster in the public cluster set as public function points, and screening a basic scene cluster from the public cluster set based on function points other than the public function points; acquiring a product set matched by the basic scene cluster, and combining products capable of realizing the public function points from the product set to obtain a product combination module; wherein the products in the product set can realize the function points in the basic scene cluster.

[0005] To solve the above technical problem, the second aspect of the present application provides an electronic device, which comprises a memory and a processor coupled with each other, wherein the memory stores program data, and the processor invokes the program data to execute the method of the first aspect.

[0006] To solve the above technical problem, the third aspect of the present application provides a computer readable storage medium, which stores program data, and the program data is executed by a processor to implement the method of the first aspect.

[0007] The above solution obtains solution requirements and decomposes them into multiple scenario clusters, wherein each scenario cluster matches a functional requirement, and the functional requirements together constitute the solution requirements. The scenario clusters include all functional points required to implement the functional requirements, thereby dividing the solution requirements into different scenarios according to the functional requirements. All scenario clusters are clustered based on the functional points in all scenario clusters, and similar scenario clusters are integrated by comparing the functional points in different scenario clusters. All scenario clusters are clustered into at least one common cluster set, wherein each common cluster set includes at least one scenario cluster. The functional points included in each scenario cluster in the common cluster set are used as common functional points. Based on the functional points in the common cluster set other than the common functional points, the scenario clusters in the common cluster set are screened, thereby screening at least some scenario clusters as basic scenario clusters, so as to obtain basic scenario clusters that are less affected by functional points other than the common functional points, so that the basic scenario clusters have a strong correlation with the common functional points. Obtain the product set that matches the basic scenario cluster, where the products in the product set can realize the functional points in the basic scenario cluster, select products that can realize common functional points from the product set and combine them to obtain a product combination module, so that the product combination module can realize universal common functional points when called, so that the product combination module can adapt to different needs and improve the versatility of the product combination. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts. Among them:

[0009] Figure 1 This is a flow chart of an embodiment of the modular product construction method of the present application;

[0010] Figure 2 This is a flow chart of another embodiment of the modular product construction method of the present application;

[0011] Figure 3 This is a schematic diagram of an application scenario of an implementation method of obtaining a public cluster set in this application;

[0012] Figure 4 It is a schematic diagram of an embodiment of a function matrix constructed based on a public cluster set in the present application;

[0013] Figure 5 This is a schematic diagram of an implementation method of obtaining a basic scene cluster in this application;

[0014] Figure 6 It is a schematic diagram of an implementation method of the functional matrix of the present application;

[0015] Figure 7 It is a schematic diagram of an embodiment of the product matrix of the present application;

[0016] Figure 8 It is a schematic diagram of an implementation method of the theoretical matching matrix of this application;

[0017] Figure 9 It is a schematic diagram of an embodiment of an actual matching matrix of the present application;

[0018] Figure 10 is a schematic diagram of an implementation method of obtaining a reference matching matrix in the present application;

[0019] Figure 11 It is a schematic diagram of an implementation method of obtaining a product combination module of the present application;

[0020] Figure 12 This is a schematic structural diagram of an embodiment of the electronic device of the present application;

[0021] Figure 13 It is a structural diagram of an embodiment of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them, and different implementation methods can be adaptively combined. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0023] The terms "system" and "network" are often used interchangeably in this document. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates that the related objects are in an "or" relationship. Furthermore, "multiple" in this document means two or more than two.

[0024] The modular product construction method provided in this application is used to perform data analysis on solution requirements, thereby constructing a universal product combination module, and its corresponding execution entity is a processing unit capable of data processing.

[0025] See also Figure 1 , Figure 1 This is a flow chart of an embodiment of a modular product construction method of the present application, which includes:

[0026] S101: Obtain solution requirements, and decompose the solution requirements into multiple scenario clusters; each scenario cluster matches a functional requirement, and the scenario cluster includes all functional points for realizing the functional requirement.

[0027] Specifically, the solution requirements are obtained and decomposed into multiple scenario clusters, where each scenario cluster is matched with functional requirements, and the functional requirements together constitute the solution requirements. The scenario cluster includes all functional points required to realize the functional requirements, thereby dividing the solution requirements into different scenarios according to the functional requirements.

[0028] It should be noted that a function point corresponds to a single function, and multiple function points within a scenario cluster are combined to implement the functional requirements of the scenario cluster. For example, if the functional requirement corresponds to forming a target set, then the function points include target identification and target classification. This application does not impose specific restrictions on the specific functional requirements and function points.

[0029] In some implementation scenarios, solution requirements are obtained, a solution architecture that matches the solution requirements is determined, the solution requirements are decomposed into functional requirements matched by each node in the solution architecture, and a scenario cluster consisting of all functional points that can realize the functional requirements is obtained.

[0030] In some implementation scenarios, solution requirements are obtained, word segmentation is performed on the solution requirements, a list of functional requirements matching the solution requirements is obtained, the list of functional requirements is traversed, and a scenario cluster consisting of functional points that can realize the functional requirements is obtained.

[0031] Optionally, at least some of the function points in the scene cluster correspond to standard function points in the function library, wherein the original function points in the scene cluster are converted into standardized standard function points in the function library according to the functional similarity between them and the standard function points, so that the function points in the scene cluster have better versatility.

[0032] S102: Clustering all scene clusters based on functional points in all scene clusters to obtain at least one common cluster set; wherein each common cluster set includes at least one scene cluster.

[0033] Specifically, all scene clusters are clustered based on the functional points in all scene clusters, so that similar scene clusters are integrated by comparing the functional points in different scene clusters, and all scene clusters are clustered into at least one common cluster set, wherein each common cluster set includes at least one scene cluster.

[0034] In some implementation scenarios, based on the functional points in the scene clusters, the inter-cluster distance between scene clusters is determined, and the scene clusters whose inter-cluster distances meet the distance conditions are clustered into the same set. The inter-cluster distances are calculated using the scene clusters in the set and the scene clusters in other sets, and the sets whose inter-cluster distances meet the distance conditions are clustered into the same set until the inter-cluster distances between all sets do not meet the distance conditions, and at least one common cluster set is obtained.

[0035] In some implementation scenarios, based on the functional points in the scene clusters, the inter-cluster distance between every two scene clusters is determined, and the scene clusters whose inter-cluster distances meet the distance condition are clustered to obtain a common cluster set formed by at least one cluster center.

[0036] It should be noted that the inter-cluster distance is used to feedback the relevance and similarity between the functional points included in the scene cluster, and its calculation method can use cosine distance or other calculation methods.

[0037] Optionally, the inter-cluster distance is calculated based on standard function points in the scene clusters, so that the scene clusters are clustered according to function points that can be standardized, ensuring that the function points included in the scene clusters in the public cluster set are universal.

[0038] S103: Acquire function points included in each scene cluster in the public cluster set as public function points, and filter out basic scene clusters from the public cluster set based on function points other than the public function points.

[0039] Specifically, the functional points included in each scene cluster in the public cluster set are taken as public functional points, and the scene clusters in the public cluster set are screened based on the functional points other than the public functional points in the public cluster set, so as to screen out at least some scene clusters as basic scene clusters, so as to obtain basic scene clusters that are less affected by functional points other than the public functional points, so that the basic scene clusters have a stronger correlation with the public functional points.

[0040] It is understandable that the function points included in each scene cluster in the public cluster set are counted to obtain the public function points, wherein when the scene cluster includes standard function points, the public function points are usually standardized standard function points.

[0041] In some implementation scenarios, the statistical number of functional points other than the common functional points in the scene cluster is obtained, and the scene cluster whose statistical number meets the quantity condition is used as the basic scene cluster, where the quantity condition can be the minimum number or the number is less than the quantity threshold, thereby reducing the impact of the basic scene cluster on the functional points other than the common functional points.

[0042] In some implementation scenarios, the correlation between function points other than common function points in the scene cluster and the common function points is obtained, wherein the correlation is negatively correlated with the statistical number of function points other than common function points, and the correlation is positively correlated with the functional similarity between function points other than common function points and the common function points, and the scene cluster whose correlation meets the correlation condition is taken as the basic scene cluster, wherein the correlation condition can be the highest correlation or the correlation is higher than a threshold, thereby improving the correlation between the basic scene cluster and the common function points.

[0043] S104: Obtain a product set that matches the basic scenario cluster, obtain products that can realize common functional points from the product set, and combine them to obtain a product combination module; wherein the products in the product set can realize the functional points in the basic scenario cluster.

[0044] Specifically, a product set matched by the basic scenario cluster is obtained, wherein the products in the product set can realize the functional points in the basic scenario cluster, and products that can realize common functional points are screened out from the product set for combination to obtain a product combination module, so that the product combination module can realize universal common functional points when called, so that the product combination module can adapt to different needs and improve the versatility of the product combination.

[0045] In some implementation scenarios, a product set consisting of all products that can realize the functional points in the basic scenario cluster is obtained, and products corresponding to common functional points in the product set are extracted and combined to obtain a product combination module.

[0046] In some implementation scenarios, a product set consisting of all products that can realize the functional points in the basic scenario cluster is obtained, a product function matrix is ​​constructed based on the functional points actually possessed by the products in the product set, products corresponding to the common functional points in the product function matrix are obtained as candidate products, other functional points possessed by the candidate products in addition to the common functional points are obtained, and the candidate products are screened based on the number of other functional points to obtain target products and combine them to obtain a product combination module. In this case, the number of other functional points possessed by the target product in addition to the common functional points is less than a threshold.

[0047] The above scheme, obtaining the scheme requirement, decomposing the scheme requirement to obtain a plurality of scene clusters, wherein each scene cluster matches a function requirement, the function requirements collectively constitute the scheme requirement, and the scene clusters include all function points required to implement the function requirements, thereby dividing the scheme requirement according to the function requirements to different scenes. All scene clusters are clustered based on the function points in all scene clusters, thereby integrating similar scene clusters by comparing the function points in different scene clusters, and clustering all scene clusters into at least one public cluster set, wherein each public cluster set includes at least one scene cluster. The function points included in each scene cluster in the public cluster set are taken as public function points, and the scene clusters in the public cluster set are screened based on the function points other than the public function points in the public cluster set, thereby screening at least part of the scene clusters as basic scene clusters, so as to obtain basic scene clusters that are less affected by function points other than the public function points, and make the basic scene clusters have strong relevance with the public function points. A product set matched with the basic scene clusters is obtained, wherein the products in the product set can realize the function points in the basic scene clusters, and the products that can realize the public function points are screened from the product set to be combined, thereby obtaining a product combination module, so that the product combination module can realize the universal public function points when called, and the product combination module can adapt to different requirements, thereby improving the universality of product combination.

[0048] Please refer to Figure 2 , Figure 2 is a flowchart of another embodiment of the modular product construction method of the present application, which comprises:

[0049] S201: obtaining a scheme requirement, decomposing the scheme requirement into a plurality of function requirements, and constructing an original cluster composed of original function points capable of realizing the function requirements.

[0050] Specifically, the scheme requirement is obtained, the scheme requirement is decomposed into a plurality of function requirements, the original function points capable of realizing the function requirements are determined, and the original function points are composed into an original cluster.

[0051] Optionally, the custom terms are added to the word segmentation dictionary, the description text corresponding to the scheme requirement is obtained, the description text is decomposed based on the word segmentation dictionary to obtain a plurality of function requirements, the function requirements are decomposed into a plurality of original function points, and the original function points are composed into an original cluster.

[0052] In a specific application scenario, the custom terms are added to the custom dictionary by using the custom function of the Chinese word segmentation system (Natural Language Processing and Information Retrieval, NLPIR), the scheme requirement is decomposed according to the functions of the scheme scene based on the custom dictionary, and a function point set O of the scene is obtained, wherein the function point set O is represented as Oa1 ={O a11 ,O a12 ,…,O a1i ,…,O a1n}, represents the set of functional points of the first scenario of the a-th solution.

[0053] S202: Obtain standard function points in the function library, determine the functional similarity between the standard function points and the original function points, and replace the corresponding original function points with standard function points whose functional similarity meets the similarity condition to obtain a scene cluster.

[0054] Specifically, the function library includes standard function points represented using standard terminology. The functional similarity between the standard function points and the original function points is calculated. When the functional similarity between the original function points and the standard function points meets the similarity criteria, the corresponding original function points are replaced with the standard function points, converting the original clusters into more standardized scenario clusters. For example, the non-standard original function points "intrusion tracking" and "intrusion detection" can be converted into the standard function point "intrusion detection." The specific function point descriptions can be determined based on the actual scenario and are not specifically limited in this application.

[0055] It should be noted that the function points in each original cluster are subjected to standard transformation and compared with the function library S. The function similarity can be calculated based on the following formula:

[0056]

[0057] Among them, α is a constant, o a1i represents the original function point in the original cluster, S j Indicates the standard function point in the function library, d indicates o a1i and S j The path length after mapping to the same vector space, sim() represents the functional similarity. When the functional similarity exceeds the similarity threshold, the function point standardization conversion is performed, that is, using S j Replace o a1i , ensuring the standardization of functional points in the scene cluster.

[0058] It is understandable that, in the standardized conversion process, at least part of the original function points in the original cluster are converted into standard function points. When some original function points are still retained in the original cluster, the final scene cluster O is obtained. a1 =O a1 ′+O a1 ″, where O a1 ′ is a standard function point combination {S1, S2, …, S i ,…,S n}, O a1 ″ is a non-standard original function point combination {Oa11 ,O a12 ,…,O a1i ,…,O a1n}.

[0059] S203: Clustering all scene clusters based on the functional points in all scene clusters to obtain at least one common cluster set; wherein each common cluster set includes at least one scene cluster.

[0060] Specifically, all scene clusters are clustered based on the functional points in all scene clusters, similar scene clusters are integrated, and all scene clusters are clustered into at least one common cluster set.

[0061] In some implementation scenarios, all scene clusters are clustered based on the functional points in all scene clusters to obtain at least one common cluster set, including: determining the inter-cluster distance between all scene clusters based on the standard functional points in all scene clusters; clustering all scene clusters based on the inter-cluster distance to obtain at least one common cluster set; wherein each common cluster set includes all standard functional points in the corresponding scene cluster.

[0062] Specifically, the standard function points in each scene cluster are obtained, and based on the standard function points in the scene cluster, the inter-cluster distance between all scene clusters is determined, and all scene clusters are clustered based on the inter-cluster distance, so that scene clusters with similar standard function points are classified into the same set. Starting from a single scene cluster, all scene clusters are continuously merged according to the inter-cluster distance to form a tree-like cluster set, and at least one public cluster set is obtained, wherein the public cluster set includes all standard function points in the corresponding scene cluster, thereby realizing the integration of standard function points.

[0063] For illustration purposes, see Figure 3 , Figure 3 This is a schematic diagram of an application scenario of an embodiment of obtaining a public cluster set in this application, wherein the standard function points in different scenario clusters include: a1 ′、O b2 ′、O c3 ′、O d4 ′、O e6 ′、O f6 ′、O g6 ' as an example, where letters a to g correspond to scenes, and numbers correspond to the nth function point in the scene, which corresponds to the standard function point. Select any two scenes' standard function point combinations such as O a1 ′、O b2 'To calculate the distance between clusters, the calculation method can be expressed by the following formula:

[0064]

[0065] Among them, d avy () represents the distance between clusters, x is O a1 'The value in the function point, y is O b2 ′The value in the function point. When the distance between scene clusters meets the clustering condition, the corresponding scene clusters are classified into the same set, and then the traversal calculation is performed to merge the scene clusters that meet the requirements until the distance between all clusters does not meet the merging requirements, and the final public cluster set is obtained. Figure 3 For the example in , we finally get the public cluster set {O a1 ′,O b2 ′,O c3 ′,O d4 ′,O e6 '} and {O f6 ′,O a6 ′}.

[0066] S204: Acquire function points included in each scene cluster in the public cluster set as public function points, and filter out basic scene clusters from the public cluster set based on function points other than the public function points.

[0067] Specifically, the functional points included in each scene cluster in the public cluster set are taken as public functional points, and the scene clusters in the public cluster set are screened based on the functional points other than the public functional points in the public cluster set, thereby screening out at least some scene clusters as basic scene clusters.

[0068] In some implementation scenarios, the functional points included in each scene cluster in the public cluster set are obtained as common functional points, and based on the functional points other than the common functional points, basic scene clusters are screened from the public cluster set, including performing the following steps on the public cluster set: obtaining the standard functional points included in each scene cluster as common functional points; obtaining the statistical number corresponding to the standard functional points other than the common functional points included in each scene cluster; and taking the scene cluster whose statistical number meets the quantity conditions as the basic scene cluster.

[0069] Specifically, for each public cluster set, the standard function points included in each scene cluster are obtained as public function points from all the scene clusters included in the public cluster set, and the number of standard function points other than the public function points included in each scene cluster is counted to obtain the statistical quantity, and the scene cluster whose statistical quantity meets the quantity condition is taken as the basic scene cluster, thereby ensuring that the basic scene cluster has a strong correlation with the public function points.

[0070] For illustration purposes, see Figure 4 and Figure 5 , Figure 4 This is a schematic diagram of an embodiment of a function matrix constructed based on a public cluster set in this application. Figure 5This is a schematic diagram of an implementation method of obtaining a basic scene cluster in this application, still using the public cluster set {O a1 ′,O b2 ′,O c3 ′,O d4 ′,O e6 ′} as an example, where the standard function points corresponding to all scene clusters in the public cluster set include S1, S2, S3, S5, S7, S8, S9, S 10 、S 11 , a two-dimensional function matrix F is constructed based on the standard function points included in all scene clusters in the public cluster set. The function matrix F is as follows Figure 4 shown.

[0071] Furthermore, we calculate the product of each column vector of the F matrix, and the function points corresponding to the non-zero column vector products are the function points S1, S3, and S7 that all scene clusters have as common function points. Then, we remove the column vectors where S1, S3, and S7 are located, and get Figure 5 The vector after removing S1, S3, and S7 is selected from the scene cluster O with the smallest sum of row vectors. a1 , O c3 , O e6 , as the basic scene cluster, that is, the number of other function points in the basic scene cluster except the public function points is the least, and the correlation with the public function points is the strongest.

[0072] S205: Obtain a product set that matches the basic scenario cluster, obtain products that can realize common functional points from the product set, and combine them to obtain a product combination module; wherein the products in the product set can realize the functional points in the basic scenario cluster.

[0073] Specifically, a product set matched by the basic scenario cluster is obtained, wherein the products in the product set can realize the functional points in the basic scenario cluster, and products that can realize common functional points are screened out from the product set for combination to obtain a product combination module.

[0074] In some implementation scenarios, a product set matched by the basic scenario cluster is obtained, and products that can realize common functional points are obtained from the product set for combination to obtain a product combination module, including: obtaining a product set matched by the basic scenario cluster, matching the products in the product set with the functional points included in the basic scenario cluster to obtain a reference matching matrix; obtaining products corresponding to the common functional points in the reference matching matrix for combination to obtain a product combination module.

[0075] Specifically, a product set consisting of products that can realize the functional points included in the basic scenario cluster is obtained, and the products in the product set are matched and verified with the functional points included in the basic scenario cluster to obtain a reference matching matrix, so as to clarify the functional points that each product can realize through the reference matching matrix.

[0076] Furthermore, products corresponding to the common function points are obtained from the reference matching matrix and combined to obtain a product combination module, so as to ensure that the products in the product combination module can realize the functions of all the common function points.

[0077] It should be noted that matching the products in the product set with the functional points included in the basic scenario cluster to obtain a reference matching matrix includes: constructing a functional matrix based on the basic scenario cluster and its corresponding functional points, and constructing a product matrix based on the basic scenario cluster and all the products in its corresponding product set; generating a theoretical matching matrix when matching functional points with products based on the functional matrix and the product matrix; obtaining an actual matching matrix between the products in the product set and the actual functional points they possess, and multiplying the elements in the theoretical matching matrix and the actual matching matrix at the same position to obtain a reference matching matrix.

[0078] Specifically, see Figure 6 , Figure 6 This is a schematic diagram of an implementation of the function matrix of the present application. The basic scene cluster itself and the function points of the basic scene cluster are used as a dimension to construct a two-dimensional function matrix F′, that is, Figure 6 The matrix shown in FIG5 , wherein the function matrix F′ includes all common function points and other standard function points, so that all standard function points included in the basic scene cluster and at least some non-standard original function points are set in the function matrix F′. Figure 6 The non-standard original function point O is schematically added a11 .

[0079] Similarly, see Figure 7 , Figure 7 This is a schematic diagram of an embodiment of the product matrix of the present application. The basic scenario cluster itself and the products required to realize the functional points in the basic scenario cluster are used as a dimension to construct a two-dimensional product matrix P, that is, Figure 7 The matrix shown.

[0080] Further, see Figure 8 , Figure 8 This is a schematic diagram of an implementation of the theoretical matching matrix of this application. The theoretical matching matrix when the function point and the product are matched is generated through the function matrix and the product matrix. P ′=P T ×F′, that is Figure 8 The matrix shown.

[0081] Please note that Figure 9 , Figure 9 This is a schematic diagram of an embodiment of the actual matching matrix of the present application. The products in the product set and the functional points actually possessed by the products are used as a dimension to construct a two-dimensional actual matching matrix G. P , that is Figure 9 The matrix shown.

[0082] Understandably, see Figure 10 , Figure 10 This is a schematic diagram of an implementation method of obtaining a reference matching matrix in this application. The elements in the theoretical matching matrix and the actual matching matrix are multiplied at the same position to obtain the following: Figure 10 The reference matching matrix shown here ensures the accuracy of the reference matching matrix by combining theoretical data and actual data.

[0083] It should be noted that obtaining products corresponding to common function points in the reference matching matrix and combining them to obtain a product combination module includes: obtaining products corresponding to common function points in the reference matching matrix as candidate products; obtaining a function point verification set that matches the candidate products in the reference matching matrix except for the common function points, and combining all candidate products when the function point verification set is an empty set to obtain a product combination module.

[0084] Specifically, see Figure 11 , Figure 11 This is a schematic diagram of an implementation method of the product combination module of this application. The products corresponding to the common function points in the reference matching matrix are obtained as candidate products. That is, the products P1, P3, and P4 corresponding to S1, S3, and S7 are obtained from the reference matching matrix as candidate products. The column vectors corresponding to the common function points in the reference matching matrix are removed. The column vectors corresponding to the common function points correspond to Figure 11 The matrix shown in the upper right corner can be obtained by removing the column vectors corresponding to the common function points. Figure 11 The matrix shown in the lower right corner is used as the check matrix.

[0085] Furthermore, the function point check set that the candidate product matches in the reference matching matrix except for the common function points is determined from the check matrix, that is, whether the sum of the row vectors of P1, P3, and P4 in the check matrix is ​​zero is determined, so that when the function point check set is an empty set, all candidate products are combined to obtain a product combination module, thereby ensuring that the products in the product combination module do not include function points other than the common function points, ensuring the matching degree between the product combination module and the common function points, and will not be affected by other function points when calling the product combination module.

[0086] It should be noted that, after obtaining the product set that matches the basic scenario cluster, obtaining products that can realize common functional points from the product set and combining them to obtain the product combination module, it also includes: in response to the need to replace any original product in the product combination module, obtaining the replacement product corresponding to the original product; based on the functional points possessed by the replacement product, testing the substitutability of the replacement product, and replacing the original product with the replacement product after the test passes.

[0087] Specifically, when any original product in the product combination module needs to be replaced, the replacement product corresponding to the original product is obtained. By comparing the function point vectors of the original product and the replacement product, it is ensured that the replacement product contains the original function points, and the compatibility between the replacement product and other products in the product combination is tested. If the product combination test results show that it can be connected with other products in the product combination module, the original product is replaced with the replacement product after the test passes, and a change process is initiated for the solution that calls the product combination module, thereby ensuring the stability of the product combination module and the accuracy of the solution that calls the product combination module.

[0088] In this embodiment, a solution requirement is obtained, decomposed into multiple functional requirements, and original function points that can realize the functional requirements are determined. The original function points are grouped into original clusters, and the corresponding original function points are replaced with standard function points to convert the original clusters into more standardized scene clusters. Based on the standard function points in the scene clusters, the inter-cluster distances between all scene clusters are determined. All scene clusters are clustered based on the inter-cluster distances, thereby grouping scene clusters with similar standard function points into the same set to obtain at least one common cluster set. From all scene clusters included in the common cluster set, the standard function points included in each scene cluster are obtained as common function points. The number of standard function points other than the common function points included in each scene cluster is counted to obtain a statistical number. The scene clusters whose statistical number meets the quantitative conditions are selected as basic scene clusters, thereby ensuring that the basic scene clusters have a strong correlation with the common function points. The products in the product set are matched and checked with the functional points included in the basic scenario cluster to obtain a reference matching matrix, so as to clarify the functional points that each product can achieve through the reference matching matrix, obtain the products corresponding to the common functional points in the reference matching matrix as candidate products, determine the function point verification set that the candidate products match in the reference matching matrix except for the common functional points, and combine all candidate products when the function point verification set is an empty set to obtain a product combination module, thereby ensuring that the products in the product combination module do not include functional points other than the common functional points, and ensuring the matching degree between the product combination module and the common functional points.

[0089] See also Figure 12 , Figure 12This is a structural diagram of an embodiment of an electronic device of the present application. The electronic device 30 includes a memory 301 and a processor 302 coupled to each other, wherein the memory 301 stores program data (not shown in the figure), and the processor 302 calls the program data to implement the method in any of the above embodiments. For an explanation of the relevant content, please refer to the detailed description of the above method embodiments, which will not be repeated here.

[0090] See also Figure 13 , Figure 13 This is a structural diagram of an embodiment of a computer-readable storage medium of the present application. The computer-readable storage medium 40 stores program data 400. When the program data 400 is executed by the processor, the method in any of the above embodiments is implemented. For an explanation of the relevant content, please refer to the detailed description of the above method embodiments, which will not be repeated here.

[0091] It should be noted that the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of this embodiment.

[0092] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0094] The above description is merely an implementation method of the present application and does not limit the scope of protection of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the scope of protection of the present application.

Claims

1. A modular product construction method, characterized in that: The method comprises: Obtaining solution requirements, and decomposing the solution requirements into multiple scenario clusters; wherein each scenario cluster matches a functional requirement, and the scenario cluster includes all functional points for realizing the functional requirement; Clustering all the scene clusters based on the functional points in all the scene clusters to obtain at least one common cluster set; wherein each of the common cluster sets includes at least one of the scene clusters; Acquire function points included in each of the scene clusters in the public cluster set as common function points, and filter out basic scene clusters from the public cluster set based on function points other than the common function points; Obtain a product set that matches the basic scenario cluster, obtain products that can realize the common functional points from the product set and combine them to obtain a product combination module; wherein the products in the product set can realize the functional points in the basic scenario cluster.

2. The modular product construction method according to claim 1, characterized in that: The acquiring solution requirements and decomposing the solution requirements into multiple scenario clusters include: Obtaining solution requirements, decomposing the solution requirements into multiple functional requirements, and constructing an original cluster consisting of original functional points that can realize the functional requirements; Acquire standard function points in a function library, determine the functional similarity between the standard function points and the original function points, and replace the corresponding original function points with the standard function points whose functional similarity satisfies a similarity condition to obtain the scene cluster.

3. The modular product construction method according to claim 2, characterized in that: The clustering of all the scene clusters based on the functional points in all the scene clusters to obtain at least one common cluster set includes: determining inter-cluster distances between all the scene clusters based on the standard function points in all the scene clusters; All the scene clusters are clustered based on the inter-cluster distance to obtain at least one public cluster set; wherein each public cluster set includes all the standard function points in the corresponding scene cluster.

4. The modular product construction method according to claim 2, characterized in that: The obtaining of the function points included in each of the scene clusters in the common cluster set as common function points, and screening the basic scene clusters from the common cluster set based on function points other than the common function points, includes performing the following steps on the common cluster set: Acquire the standard function points included in each of the scene clusters as common function points; Obtaining a statistical quantity corresponding to the standard function points other than the common function points included in each of the scene clusters; The scene clusters whose statistical quantity meets the quantity condition are used as the basic scene clusters.

5. The modular product construction method according to claim 1, characterized in that: The step of obtaining a product set matched by the basic scenario cluster, obtaining products capable of realizing the common functional point from the product set and combining them to obtain a product combination module includes: Acquire a product set matched by the basic scenario cluster, match the products in the product set with the functional points included in the basic scenario cluster, and obtain a reference matching matrix; The products corresponding to the common functional points in the reference matching matrix are obtained and combined to obtain the product combination module.

6. The modular product construction method according to claim 5, characterized in that: The matching of the products in the product set with the functional points included in the basic scenario cluster to obtain a reference matching matrix includes: Based on the basic scenario clusters and their corresponding functional points, a function matrix is ​​constructed; based on the basic scenario clusters and all the corresponding products in the product set, a product matrix is ​​constructed; Based on the function matrix and the product matrix, a theoretical matching matrix is ​​generated when the function points are matched with the product; An actual matching matrix between products in the product set and actual functional points is obtained, and the elements in the theoretical matching matrix and the actual matching matrix are multiplied in the same position to obtain the reference matching matrix.

7. The modular product construction method according to claim 5, characterized in that: The obtaining and combining products corresponding to the common function points in the reference matching matrix to obtain the product combination module includes: Acquire products corresponding to the common function points in the reference matching matrix as candidate products; A function point check set matched by the candidate products in the reference matching matrix except for the common function points is obtained, and when the function point check set is an empty set, all the candidate products are combined to obtain the product combination module.

8. The modular product construction method according to claim 1, characterized in that: After obtaining a product set matched by the basic scenario cluster, obtaining products capable of realizing the common functional point from the product set and combining them to obtain a product combination module, the process further includes: In response to any original product in the product combination module needing to be replaced, obtaining a replacement product corresponding to the original product; Based on the functional points of the substitute product, the substitutability of the substitute product is tested, and after passing the test, the substitute product is used to replace the original product.

9. An electronic device, characterized in that: include: A memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having program data stored thereon, characterized in that: When the program data is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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