CN111523764B

CN111523764B - Service architecture detection method, device, tool, electronic equipment and medium

Info

Publication number: CN111523764B
Application number: CN202010213298.XA
Authority: CN
Inventors: 霍嘉; 阮姗; 殷宇; 陈璐璐
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2020-03-24
Filing date: 2020-03-24
Publication date: 2022-05-27
Anticipated expiration: 2040-03-24
Also published as: CN111523764A

Abstract

The disclosure provides a service architecture detection method, a device, a tool, an electronic device and a medium. The service architecture detection method comprises the following steps: responding to an activity model identification from a client, determining business data and a common variable factor, and sending the common variable factor to the client, wherein the activity model corresponding to the activity model identification comprises a task model, and the common variable factor is a variable shared by a plurality of task models; responding to the public variable factor identification to be detected from the client, determining the business data to be detected from the business data based on the public variable factor identification to be detected, wherein the public variable factor to be detected corresponding to the public variable factor identification to be detected is in the public variable factors; and performing block point analysis on the service data to be detected to obtain a block point part of the service architecture, wherein the number of services processed by the block point part of the service architecture meets a block point judgment condition relative to the number of services processed by parts except the block point part in the service architecture.

Description

Business architecture detection method, device, tool, electronic equipment and medium

Technical Field

The present disclosure relates to the field of internet technologies, and in particular, to a method, an apparatus, a tool, an electronic device, and a medium for detecting a service architecture.

Background

Enterprises need to check business architecture to ensure that business processes can be executed as expected.

In implementing the disclosed concept, the inventors found that there are at least the following problems in the related art: in the process of detecting the business architecture by an enterprise, the acquisition of business data and the analysis of a large amount of business data are involved, which has high requirements on the business knowledge of analysts, the storage of acquisition technology and the mastering of a data analysis method, and the problems of omission, deficiency and the like easily occur in the acquisition of the business data, so that the detection result is inaccurate.

Disclosure of Invention

In view of this, the present disclosure provides a service architecture detection method, apparatus, tool, electronic device, and medium, which can reduce detection difficulty and improve detection accuracy.

One aspect of the present disclosure provides a method for detecting a service architecture, where the service architecture includes a process model, the process model includes an activity model, and the method includes: responding to an activity model identification from a client, determining business data and a common variable factor, and sending the common variable factor to the client, wherein the activity model corresponding to the activity model identification comprises a task model, and the common variable factor is a variable shared by a plurality of task models; responding to the public variable factor identification to be detected from the client, determining the business data to be detected from the business data based on the public variable factor identification to be detected, wherein the public variable factor to be detected corresponding to the public variable factor identification to be detected is in the public variable factors; and performing block point analysis on the service data to be detected to obtain a block point part of the service architecture, wherein the number of services processed by the block point part of the service architecture meets a block point judgment condition relative to the number of services processed by parts except the block point part in the service architecture.

The business architecture detection method provided by the embodiment of the disclosure takes a business architecture as a detection starting point, obtains the full business data associated with an activity model of the business architecture from an Internet Technology (IT) architecture, and can reflect the business process of an enterprise in a full view angle because the business architecture is constructed based on the business process of the enterprise, so that a tester can obtain the full business data corresponding to the activity model based on the business architecture, the risk of missing or lacking in the collection of the business data is reduced, and the requirements on the business knowledge and the storage of the collection technology of the tester are greatly reduced. In addition, the block point of the service architecture is mined based on the service quantity corresponding to the activity model, and the block point is a block point which influences service continuation or data transmission in the service data stream, reflects the pain point of a part of services to a certain extent, and is analyzed to determine the block point part of the service architecture so as to improve the service flow. In addition, the method further reduces the requirements on the reserves of the service knowledge and the technical knowledge of the testers and the data analysis method by analyzing the block point part of the service architecture through the block point.

According to the embodiment of the present disclosure, the performing a blocking point analysis on the service data to be detected to obtain a blocking point part of the service architecture includes: clustering the service data to be detected to obtain respective service quantity of a plurality of categories, wherein the categories respectively correspond to at least part of models in the process model; and determining whether at least a portion of the model corresponding to each of the plurality of classes is a choke point portion based on the amount of traffic for each of the plurality of classes.

According to the embodiment of the present disclosure, clustering the service data to be detected to obtain the respective service quantities of the multiple categories includes: and clustering the service data to be detected based on at least one specified statistical dimension to obtain the service quantity of each of the at least one specified statistical dimension.

According to the embodiment of the disclosure, at least one specified statistical dimension is determined based on the public information of the service data to be detected.

According to an embodiment of the present disclosure, when models corresponding to each of a plurality of categories are in a serial relationship in an active model, the blocking point determination condition includes that the traffic volume of the category is higher than a first reference threshold; and/or when the models corresponding to the plurality of classes are in a parallel relationship in the active model, the blocking point judgment condition includes that the number of the traffics of the class is lower than a second reference threshold.

According to the embodiment of the disclosure, the business architecture further comprises an entity model, and a first corresponding relation exists between the task model and the entity model. Accordingly, determining the traffic data includes: determining an associated task model included in the activity model corresponding to the activity model identification; determining the associated transaction service corresponding to the associated task model from the Internet technology architecture, wherein a second corresponding relation exists between the task model and the transaction service of the Internet technology architecture; determining an associated object service corresponding to the associated transaction service, wherein a third corresponding relation exists between the transaction service and the object service; and determining business data generated by the associated object service.

According to an embodiment of the present disclosure, the method further includes: after the service data to be detected is subjected to the point blocking analysis to obtain a point blocking part of the service architecture, the abnormal point blocking part is removed from the point blocking part of the service architecture based on the service rule.

According to an embodiment of the present disclosure, the method further includes: after the service data to be detected is subjected to the block point analysis to obtain a block point part of the service architecture, a detection report is generated, wherein the detection report comprises the block point part of the service architecture.

According to an embodiment of the present disclosure, generating the detection report includes: replacing each parameter in the detection report template to generate a detection report packet, wherein the parameter of the detection report template comprises at least one of the following: title, synopsis, content type, specific content template, and dynamic parameters.

Another aspect of the present disclosure provides a method for detecting a service architecture, where the service architecture includes a process model, the process model includes an activity model, and the method includes: sending an activity model identifier to a server side so that the server side can determine business data and a common variable factor and send the common variable factor to a client side, wherein an activity model corresponding to the activity model identifier comprises a task model, and the common variable factor is a variable shared by a plurality of task models; sending the public variable factor identifier to be detected to the server so that the server determines the service data to be detected from the service data based on the public variable factor identifier to be detected, wherein the public variable factor to be detected corresponding to the public variable factor identifier to be detected is in the public variable factors; and receiving a detection report from the server, wherein the detection report comprises the information of a point blocking part, and the information of the point blocking part is determined by the server by performing point blocking analysis on the service data to be detected, wherein the service quantity processed by the point blocking part of the service framework meets the point blocking judgment condition relative to the service quantity processed by the part except the point blocking part of the service framework.

Another aspect of the present disclosure provides a service architecture detection apparatus, including: the device comprises a first response module, a second response module and an analysis module. The first response module is used for responding to an activity model identifier from a client, determining business data and a public variable factor, and sending the public variable factor to the client, wherein the activity model corresponding to the activity model identifier comprises a task model, and the public variable factor is a variable shared by a plurality of task models; the second response module is used for responding to the public variable factor identifier to be detected from the client, determining the service data to be detected from the service data based on the public variable factor identifier to be detected, wherein the public variable factor to be detected corresponding to the public variable factor identifier to be detected is in the public variable factors; and the analysis module is used for performing block point analysis on the service data to be detected to obtain a block point part of the service architecture, wherein the service quantity processed by the block point part of the service architecture meets a block point judgment condition relative to the service quantity processed by the part except the block point part of the service architecture.

Another aspect of the present disclosure provides a traffic architecture detection apparatus, including a first sending module, a second sending module, and a report receiving module. The first sending module is used for sending an activity model identifier to the server so that the server determines service data and a common variable factor and sends the common variable factor to the client, wherein the activity model corresponding to the activity model identifier comprises a task model, and the common variable factor is a variable shared by a plurality of task models; the second sending module is used for sending the public variable factor identifier to be detected to the server side so that the server side can determine the service data to be detected from the service data based on the public variable factor identifier to be detected, wherein the public variable factor to be detected corresponding to the public variable factor identifier to be detected is in the public variable factors; and a report receiving module, configured to receive a detection report from the server, where the detection report includes information of a block point part, and the information of the block point part is determined by performing block point analysis on the service data to be detected by the server, where a service quantity processed by the block point part of the service framework satisfies a block point determination condition with respect to a service quantity processed by a part of the service framework other than the block point part.

Another aspect of the present disclosure provides a business architecture detection tool, comprising: the system comprises a framework asset positioning module, a data acquisition module, a dimension generation module and a detection module. The architecture asset positioning module is used for searching the affiliated process model in the business architecture asset library according to the activity model identification and outputting the common variable factor related to all task models included in the process model; the data acquisition module is used for searching business data generated by all the task models according to the entity models corresponding to all the task models and screening the business data according to public variable factors selected by a user; the dimension generation module is used for taking the extracted public information as a statistical dimension by searching a database table for recording the business data; the detection module is used for clustering the service data according to the statistical dimensionality to obtain a point blocking part of the service architecture.

According to an embodiment of the present disclosure, the business architecture detection tool further includes a diagnostic book generation module for generating a diagnostic book, the diagnostic book including a resistance point portion of the business architecture.

According to an embodiment of the present disclosure, the business architecture detection tool further includes a diagnostic book model setting module, the diagnostic book model setting module is configured to define formatted contents of the diagnostic book to generate the diagnostic book according to the formatted contents, and the formatted contents include at least one of: content type, dynamic parameters, and code snippet of presentation.

Another aspect of the present disclosure provides an electronic device comprising one or more processors and a storage for storing 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 for implementing the method as described above when executed.

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

Drawings

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

FIG. 1 schematically illustrates an exemplary system architecture to which business architecture detection methods, apparatus, tools, electronic devices, and media may be applied, in accordance with embodiments of the present disclosure;

FIG. 2 schematically illustrates a logic diagram of a traffic architecture detection method according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow diagram of a resource handling business architecture detection method according to an embodiment of the present disclosure;

FIG. 4 schematically illustrates a structural schematic of a business architecture and an IT architecture, in accordance with an embodiment of the present disclosure;

FIG. 5 schematically illustrates a resistance point diagram of a structure of a series relationship according to an embodiment of the disclosure;

FIG. 6 schematically illustrates a block point diagram of a structure of a parallel relationship according to an embodiment of the disclosure;

FIG. 7 schematically illustrates a dimensional sub-channel visualization business indicator diagram according to an embodiment of the disclosure;

FIG. 8 schematically illustrates a dimensional sub-channel visualization business indicator diagram according to another embodiment of the present disclosure;

FIG. 9 schematically shows a schematic view of a diagnostic book according to an embodiment of the present disclosure;

FIG. 10 schematically illustrates a flow diagram of a resource handling business architecture detection method according to another embodiment of the present disclosure;

FIG. 11 schematically shows a block diagram of a traffic architecture detection apparatus according to an embodiment of the present disclosure;

FIG. 12 schematically shows a block diagram of a traffic architecture detection apparatus according to another embodiment of the present disclosure;

FIG. 13 schematically illustrates a block diagram of a business architecture detection tool in accordance with an embodiment of the present disclosure;

FIG. 14 schematically illustrates a data flow diagram of a business architecture detection tool in accordance with an embodiment of the present disclosure; and

FIG. 15 schematically shows 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 illustrative only 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 disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

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 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.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have 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.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have 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 "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features.

The embodiment of the disclosure provides a service architecture detection method, a service architecture detection device, a service architecture detection tool, electronic equipment and a service architecture detection medium. The service architecture detection method comprises a service data to be detected determining process and a blocking point analyzing process. In the process of determining the to-be-detected service data, the service data and the public variable factor are determined in response to the activity model identification from the client, the public variable factor is sent to the client, and then the to-be-detected service data is determined from the service data based on the to-be-detected public variable factor identification from the client. After the process of determining the service data to be detected is completed, a block point analysis process is entered, and block point analysis is performed on the service data to be detected to obtain a block point part of the service framework, wherein the number of services processed by the block point part of the service framework meets a block point judgment condition relative to the number of services processed by parts of the service framework except the block point part.

Fig. 1 schematically illustrates an exemplary system architecture to which business architecture detection methods, apparatuses, tools, electronic devices and media may be applied, according to embodiments of the disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

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

terminal devices

101, 102, 103, a network 104, and

servers

105, 106. The network 104 may include a plurality of gateways, routers, hubs, network wires, etc. to provide a medium for communication links between the

terminal devices

101, 102, 103 and the

servers

105, 106. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may use the

terminal devices

101, 102, 103 to interact with other terminal devices and

servers

105, 106 via the network 104 to receive or transmit information or the like. The

terminal devices

101, 102, 103 may be installed with various communication client applications, such as an enterprise management application, a banking application, a cloud platform application, a web browser application, a search-type application, an office-type application, an instant messaging tool, a mailbox client, a shopping-type application, social platform software, etc. (just examples).

The

terminal devices

101, 102, 103 include, but are not limited to, smart phones, virtual reality devices, augmented reality devices, tablets, laptop computers, and the like.

The server 105 may provide a blocking point analysis service for the business architecture or the like, for example, determine whether each model is a blocking point portion of the business architecture based on the business amount of the business data corresponding to each model of the business architecture. The server 106 may provide data services, for example, may provide a business architecture asset database to query, locate, update, etc. business architecture assets. The

servers

105, 106 may be database servers, background management servers, server clusters, and the like. The

servers

105 and 106 may analyze and process the received data such as the network traffic information, and feed back a processing result (such as a service architecture detection result) to the terminal device. The servers 106 and 107 may be the same server or different servers.

It should be noted that the service architecture detection method provided by the embodiment of the present disclosure may be generally executed by the server 105 or the

terminal devices

101, 102, and 103. Accordingly, the service architecture detection apparatus provided by the embodiment of the present disclosure may be generally disposed in the server 105.

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

To facilitate understanding of the service architecture detection method according to the embodiment of the present disclosure, a service architecture detection logic is first described.

Fig. 2 schematically shows a logic diagram of a service architecture detection method according to an embodiment of the present disclosure.

As shown in FIG. 2, a user locates at a client (e.g., via a tool interface) a business architecture asset to be detected, e.g., a business process that the user needs to detect is an activity that corresponds to an activity model in the business architecture. The user can obtain the process model to which the activity model belongs in the business architecture through the activity model (the process model includes a plurality of models, such as other activity models, task models included in each activity model, and the like, and the activity model has an association relationship with each other), so that all task models having an association relationship with the activity model can be obtained, the business data obtained by querying based on all the task models is more comprehensive, and the user does not need to be familiar with the whole business architecture. And then, extracting the common variable factors from all the task models, and displaying the common variable factors to a user through a tool interface so that the user can select the common variable factors to be screened and the service data of the common variable factors. Meanwhile, the server side can determine selectable statistical dimensions based on public information of the business data, the statistical dimensions can be indexes with different granularities from the public variable factors, a user can conveniently analyze the business data according to self requirements and aiming at the needed granularities so as to determine the blocking points of the business architecture, and then a business process diagnostic book is generated based on the blocking points of the business architecture so as to guide an enterprise to optimize the business process.

Wherein the tool interface may be an interactive interface of a client of the user. The process model may be stored in a business architecture asset database of the data server. The business data can be the business data generated by the enterprise in the process of business processing and is stored in a data server of the enterprise. The business architecture detection method can be executed by a specific server, such as a background server, and the like, and can also be shared with other servers of an enterprise.

It should be noted that although the variable factor is output by the system-associated process model after the user selects an activity, the selection of the variable factor and the activity in time sequence is on the same life line (the life line represents the existence of an object in the time sequence chart in a period) for the user, that is, the user selects the activity and the process model output variable factor associated with the activity are completed synchronously, and a large amount of data processing in the background of the system is not sensible to the user. Therefore, the method and the device can ensure that the selection operation of the user does not influence the server to inquire the service data, reduce the risk of service data omission and prompt the accuracy of the test result.

Fig. 3 schematically shows a flowchart of a resource handling business architecture detection method according to an embodiment of the present disclosure.

As shown in fig. 3, the method may include operations S301 to S305. In this embodiment, the business architecture includes a process model, which includes an activity model.

In order to facilitate understanding of the service architecture detection method according to the embodiment of the present disclosure, a service architecture is exemplarily described.

FIG. 4 schematically shows a structural schematic of a business architecture and an IT architecture according to an embodiment of the disclosure.

As shown in fig. 4, the business architecture includes a product model, a process model, and an entity model, where there is a correspondence between the process model and the entity model, and an entity corresponding to the entity model is used in executing a business process corresponding to the process model. The process model comprises an activity model (activity), a task model (task group) and a component model (task component), wherein the activity model, the task model and the component model provide services for entities corresponding to the entity model, a one-to-one or one-to-many first mapping relation exists between the activity model and the task model, and a one-to-one or one-to-many second mapping relation exists between the task model and the component model. The internet technology architecture (IT architecture) comprises a use case, an application transaction service, an application component service and a business object service, wherein the business object service performs read-write operation on data related to the entity model in a data set. There is a mapping relationship between the business architecture and the IT architecture, including but not limited to at least one of: a third mapping between use cases (including interactive components) and activity models, a fourth mapping between application transaction services and task models, and a fifth mapping between application component services and component models. Wherein m and n are positive integers of 1 or more, and m and n may be the same or different.

Through the business architecture and the IT architecture, each service of the IT architecture can be well corresponding to the business architecture (constructed for the business process of the enterprise) of the enterprise, and the management is convenient.

As shown in fig. 3, in operation S301, in response to an activity model identification from a client, business data and a common variable factor are determined, and the common variable factor is transmitted to the client, wherein an activity model corresponding to the activity model identification includes a task model, and the common variable factor is a variable common to a plurality of task models.

In this embodiment, the activity model identification may be an activity name or the like. For example, the user specifies a target activity in the flow model (e.g., enters an identification of the target activity, or clicks on a component of the interaction interface where the user selects the activity model, etc.). The server determines all task models having an association relation with the target activity based on the service architecture shown in fig. 4 according to the target activity determined by the user, then determines common variable factors related to all task models having the association relation, and sends the common variable factors to the client so as to facilitate selection of the client. For example, common variable factors include, but are not limited to: products, channels, partners, etc. Meanwhile, after the user specifies the target activity in the flow model, the server side also determines all the business data which are associated with the target activity. Specifically, the business data generated by each task is searched according to the entity having the mapping relationship with each task model in the process model, and as shown in fig. 4, the business data may be determined based on the business object service.

In one embodiment, the business architecture further comprises an entity model, and a first corresponding relation exists between the task model and the entity model. Accordingly, determining the traffic data may include the following operations.

First, an associated task model included by an activity model corresponding to an activity model identification is determined.

Then, an associated transaction service (refer to the application transaction service in fig. 4) corresponding to the associated task model is determined from the internet technology architecture, and a second corresponding relationship exists between the task model and the transaction service of the internet technology architecture. If the task model also includes a component model, an associated component model corresponding to the associated task model can be determined and application component services corresponding to the associated component model can be determined from the internet technology architecture.

Next, it is determined that an associated object service (refer to the business object service in fig. 4) corresponds to the associated transaction service, and a third correspondence exists between the transaction service and the object service. If it is determined that an application component service corresponding to the association component model exists, an association object service corresponding to the application component service is determined.

Then, business data generated by the associated object service is determined.

Based on the process, the user does not need to be familiar with the whole business architecture, only needs to input the activity identifier to be detected and the like, the server side can determine the business data corresponding to the activity identifier to be detected, the obtained business data can be more comprehensive, and the difficulty of inquiring the business data is reduced.

In operation S303, in response to the to-be-detected public variable factor identifier from the client, the to-be-detected service data is determined from the service data based on the to-be-detected public variable factor identifier, where the to-be-detected public variable factor corresponding to the to-be-detected public variable factor identifier is in the public variable factor.

And screening and collecting the business data through a common variable factor (the variable factor is a variable influencing each step of the process in the execution process, such as channels, products, customers and the like belong to the variable factor, and the common variable factor is a variable factor related to each task under a specified activity).

For example, after the server side sends the public variable factors to the client side, the user can select one or more of the public variable factors at the client side according to the own requirements to determine the analysis range, so as to realize screening of the service data corresponding to the activity identifier to be detected.

In operation S305, a blocking point analysis is performed on the service data to be detected to obtain a blocking point portion of the service framework, where a number of services processed by the blocking point portion of the service framework satisfies a blocking point determination condition with respect to a number of services processed by a portion of the service framework other than the blocking point portion.

In this embodiment, performing a blocking point analysis on the service data to be detected to obtain a blocking point portion of the service architecture may include the following operations.

Firstly, clustering service data to be detected to obtain respective service quantities of a plurality of categories, wherein the plurality of categories respectively correspond to at least part of models in a process model. At least part of the flow model can be a task model, a component model and the like. Due to the corresponding relation between the business architecture and the IT architecture, the business data generated by the IT architecture can be well corresponding to the model in the business architecture. The clustering may be performed in a supervised clustering manner, such as making the clustered class correspond to a preset class. Thus, the service quantity included in each preset category can be determined, so that whether the service processing capacity of each module in the service architecture is consistent with the expectation or not can be analyzed based on the service quantity. In addition, an unsupervised clustering mode can be adopted, and clustering results can also be well corresponding to a model of a business architecture.

Then, it is determined whether at least a portion of the model corresponding to each of the plurality of classes is a choke point portion based on a traffic volume of each of the plurality of classes.

Specifically, clustering the service data to be detected to obtain the respective service quantities of the multiple categories may include: and clustering the service data to be detected based on at least one specified statistical dimension to obtain the service quantity of each of the at least one specified statistical dimension.

Wherein, at least one specified statistical dimension can be determined based on the public information of the service data to be detected.

For example, a dynamic visual full-dimension index is automatically generated according to business data and data table rules, wherein statistical dimensions can be automatically generated according to business data common fields (e.g., automatically generated by business data table common fields). Statistical dimensions can also be preset, such as: user statistical dimension: sex, age, etc. User behavior dimension: registered users, user preferences, user interests, user churn, etc. Consumption dimension: amount consumed, frequency consumed, level consumed, etc. The commodity dimension is as follows: commodity type, commodity brand, commodity attribute, etc. The unit of the statistical dimension is set according to the parameter, and the median rises to the next level unit after reaching the preset threshold value, for example: the unit of the traffic is defaulted to be 'pen', and the unit is automatically switched to 'ten thousand pens' when the traffic is more than 10 ten thousand and less than ten million through parameter setting, and the unit is switched to 'one hundred thousand pens' after the traffic is more than ten million.

The following describes the analysis of the resistance point based on the result of the clustering.

Determining whether at least a portion of the model corresponding to each of the plurality of classes is a choke point portion based on the amount of traffic for each of the plurality of classes may include the following two scenarios.

For example, when models corresponding to each of the plurality of categories are in a serial relationship in the active model, the blocking point determination condition includes that the traffic volume of the category is higher than the first reference threshold.

Fig. 5 schematically illustrates a resistance point diagram of a structure of a series relationship according to an embodiment of the disclosure.

As shown in FIG. 5, the business process based on the enterprise may determine that

modules

1, 2, 3, and 4 are in serial relationship, for example, a business data processing process needs to pass through

modules

1, 2, 3, and 4. The number of services to be processed (or the number of services currently processed) of the module 3 is higher than a first reference threshold (which may be determined according to other modules, such as statistics on historical service numbers of the module 1, the module 2, and the module 4, or determined based on real-time service numbers of the module 1, the module 2, and the module 4), and if the number of services to be processed of the module 3 is significantly greater than that of other modules, it may be preliminarily determined that the module 3 is a blocking point part in the whole service flow.

For another example, when the models corresponding to the respective classes are in a parallel relationship in the active model, the blocking point determination condition includes that the number of traffics of the class is lower than the second reference threshold.

Fig. 6 schematically shows a block point diagram of a structure of a parallel relationship according to an embodiment of the present disclosure.

As shown in fig. 6, the business process based on the enterprise may determine that module 1, module 2, module 3, and module 4 are in a parallel relationship, for example, one business data may be processed by using any one of module 1, module 2, module 3, and module 4. If the number of the to-be-processed services of the module 2 is lower than the second reference threshold (which may be determined by counting the historical service numbers of the

modules

1, 3, and 4 or based on the real-time service numbers of the

modules

1, 3, and 4 in other modules), and if the number of the to-be-processed services of the module 2 is significantly smaller than that of the other modules and the tasks allocated to the modules are not significantly different, it may be determined preliminarily that the module 2 is a blocking point part in the whole service flow.

It should be noted that one activity model may include multiple task models, and a serial relationship and a parallel relationship may exist between the multiple task models at the same time. One task model has a serial relationship with other task modules in one activity model and a parallel relationship with other task modules in another activity model.

In another embodiment, the method may further include the following operations.

After the service data to be detected is subjected to the point blocking analysis to obtain a point blocking part of the service architecture, the abnormal point blocking part is removed from the point blocking part of the service architecture based on the service rule. Since the business rules may determine that there is a significant difference between the business quantity of a certain module and other modules, it cannot be determined as a blocking point part. Therefore, after the preliminary determination of the resistance point portions, abnormality exclusion may be performed on each resistance point portion based on the business rule to improve the accuracy of the resistance point analysis. It should be noted that, in addition to removing the abnormal block point part based on the business rule to improve the accuracy of the detection result, the abnormal data removal may be performed on the business data to improve the accuracy of the detection result. For example, the service data generated in some special time periods or some special areas may be greatly different from the service data generated in ordinary time periods or ordinary areas due to an emergency, and may cause an exception to some modules. At this time, these abnormal data need to be removed.

In another embodiment, to facilitate user awareness of the chokepoint portion of the traffic architecture, a detection report may be generated for review by the user.

For example, the method may further include the following operations: after the service data to be detected is subjected to the block point analysis to obtain a block point part of the service architecture, a detection report is generated, wherein the detection report comprises the block point part of the service architecture.

Specifically, generating the detection report may include replacing each parameter in the detection report template to generate the detection report packet, where the parameter of the detection report template includes at least one of: title, synopsis, content type, specific content template, and dynamic parameters. In order to improve readability and intuitiveness of the detection report, visualization processing can be performed on information such as data in the detection report, for example, contents such as data are displayed in a form of a graph.

The service architecture detection method according to the embodiment of the present disclosure is exemplarily described below with a specific embodiment. Fig. 7 schematically illustrates a dimension-based sub-channel visualization business indicator diagram according to an embodiment of the disclosure. Fig. 8 schematically illustrates a dimension-based sub-channel visualization business indicator diagram according to another embodiment of the present disclosure.

After a user selects X activity under a certain value flow in a field from the process model, the server end determines and outputs: and screening common variable factors such as conditions, dimensions, units and the like for selection by a user. The user selects the Z product of the Y channel to filter the service data under the conditions of channel and product screening, and the user selects the service state and the time as index dimensions. The server side performs table data extraction and display according to the business rules, and the schematic diagrams of the visual business indexes are shown in fig. 7 and 8. The time that the server side analyzes the service data quality is the best is the month E, the judgment of the data quality depends on the data statistical rule and the service popularization condition according to the fact that the month belongs to the service formal popularization complete month, the data quantity suddenly-dropping condition obviously occurs in

states

2, 3 and 5 according to the data analysis blocking point part of the month E, and the state 5 is an abnormal state (abnormal data can be defined according to the service preset rule). Thus, it can be preliminarily determined that there is a checkpoint suspicion for

states

2 and 3, thus partially locating the checkpoint as

states

2 and 3.

Fig. 9 schematically shows a schematic view of a diagnostic book according to an embodiment of the present disclosure.

As shown in fig. 9, in the above embodiment, after the stop point part is located in the state 2 and the state 3, the diagnosis book can be automatically issued by the server side: and the service link of the Y-channel Z product under the X activity is blocked in the service links of the state 2 and the state 3, and the service processing flows of the two blocked part need to be improved. The dimension 1 plot of fig. 9 is shown with reference to fig. 7, and the dimension 2 plot is shown with reference to fig. 8.

Fig. 10 schematically shows a flowchart of a resource handling business architecture detection method according to another embodiment of the present disclosure. The business architecture includes a process model that includes an activity model.

As shown in fig. 10, the method may be executed by a client, and specifically may include operations S1001 to S1005.

In operation S1001, an activity model identifier is sent to the server side, so that the server side determines the business data and a common variable factor, and sends the common variable factor to the client side, wherein an activity model corresponding to the activity model identifier includes a task model, and the common variable factor is a variable common to a plurality of task models.

In operation S1003, the public variable factor identifier to be detected is sent to the server, so that the server determines the service data to be detected from the service data based on the public variable factor identifier to be detected, where the public variable factor to be detected corresponding to the public variable factor identifier to be detected is in the public variable factors.

In operation S1005, a detection report from the server is received, where the detection report includes information of a blocking point portion, and the information of the blocking point portion is determined by performing a blocking point analysis on service data to be detected by the server, where a service quantity processed by the blocking point portion of the service framework satisfies a blocking point determination condition with respect to a service quantity processed by a portion other than the blocking point portion of the service framework.

The contents related to the server in operations S1001 to S1005 refer to the above contents, and are not described in detail here.

The business data of the enterprise are acquired and analyzed from the infrastructure assets based on the business architecture system of the enterprise, a dynamic visual index system is formed by adopting a big data analysis technology, business pain points are excavated, if the business data of the enterprise are captured in real time according to the requirements of testers to evaluate the operation condition of the business process, and the blocking points influencing the smooth completion of the business process are analyzed, so that the optimization and the promotion of the infrastructure assets are guided based on the results of the blocking point analysis, and the purposes of enabling the value increase of the enterprise and business innovation are achieved.

Another aspect of the present disclosure also provides a service architecture detection apparatus.

Fig. 11 schematically shows a block diagram of a traffic architecture detection apparatus according to an embodiment of the present disclosure.

As shown in fig. 11, the architecture detection apparatus 1100 may include: a first response module 1110, a second response module 1120, and an analysis module 1130.

The first response module 1110 is configured to determine, in response to the activity model identifier from the client, the business data and a common variable factor, and send the common variable factor to the client, where the activity model corresponding to the activity model identifier includes a task model, and the common variable factor is a variable common to a plurality of task models.

The second response module 1120 is configured to, in response to the to-be-detected public variable factor identifier from the client, determine the to-be-detected service data from the service data based on the to-be-detected public variable factor identifier, where the to-be-detected public variable factor corresponding to the to-be-detected public variable factor identifier is in the public variable factor.

The analysis module 1130 is configured to perform a blocking point analysis on the service data to be detected to obtain a blocking point portion of the service framework, where a number of services processed by the blocking point portion of the service framework satisfies a blocking point determination condition with respect to a number of services processed by a portion of the service framework other than the blocking point portion.

Fig. 12 schematically shows a block diagram of a traffic architecture detection apparatus according to another embodiment of the present disclosure.

As shown in fig. 12, the architecture detection apparatus 1200 includes: a first transmitting module 1210, a second transmitting module 1220, and a report receiving module 1230.

The first sending module 1210 is configured to send an activity model identifier to the server, so that the server determines service data and a common variable factor, and sends the common variable factor to the client, where an activity model corresponding to the activity model identifier includes a task model, and the common variable factor is a variable common to multiple task models.

The second sending module 1220 is configured to send the public variable factor identifier to be detected to the server, so that the server determines the service data to be detected from the service data based on the public variable factor identifier to be detected, where the public variable factor to be detected corresponding to the public variable factor identifier to be detected is in the public variable factors.

The report receiving module 1230 is configured to receive a detection report from the server, where the detection report includes information of a blocking point portion, and the information of the blocking point portion is determined by performing a blocking point analysis on service data to be detected by the server, where a service quantity processed by the blocking point portion of the service framework satisfies a blocking point determination condition with respect to a service quantity processed by a portion of the service framework other than the blocking point portion.

The operations performed by the first response module 1110, the second response module 1120, and the analysis module 1130 may refer to operations S301 to S305, and the operations performed by the first sending module 1210, the second sending module 1220, and the report receiving module 1230 may refer to operations S1001 to S1005, which are not described herein again.

Another aspect of the present disclosure also provides a business architecture detection tool.

FIG. 13 schematically shows a block diagram of a business architecture detection tool according to an embodiment of the disclosure.

As shown in fig. 13, the business architecture detection tool 1300 may include: an architecture asset location module 1310, a data collection module 1320, a dimension generation module 1330, and a detection module 1340.

The architecture asset location module 1310 is configured to search the business architecture asset library for the process model according to the activity model identifier, and output a common variable factor related to all task models included in the process model.

The data collection module 1320 is configured to search the service data generated by all task models according to the entity models corresponding to all task models, and filter the service data according to the common variable factor selected by the user. Specifically, as shown in table 1.

TABLE 1

The dimension generating module 1330 is configured to retrieve the database table for recording the business data, and use the extracted public information as a statistical dimension. Specifically, as shown in table 2.

TABLE 2

The detecting module 1340 is configured to cluster the service data according to the statistical dimensionality to obtain a blocking point portion of the service architecture. Specifically, as shown in table 3.

TABLE 3

In other embodiments, the business architecture detection tool 1300 may further include at least one of: the diagnostic book generating module and the diagnostic book model setting module.

The diagnostic book generation module is used for generating a diagnostic book, and the diagnostic book comprises a resistance point part of the business architecture. Specifically, as shown in table 4.

TABLE 4

The diagnostic book model setting module is used for defining the formatted content of the diagnostic book so as to generate the diagnostic book according to the formatted content, and the formatted content comprises at least one of the following: content type, dynamic parameters, and code snippet of presentation. Content types such as numbers, charts, text, etc. Specifically, as shown in table 5.

TABLE 5

FIG. 14 schematically shows a data flow diagram of a business architecture detection tool in accordance with an embodiment of the present disclosure.

As shown in fig. 14, a user conducts architecture asset location at a client. And the server side responds to the positioned architecture assets, performs data acquisition, automatic extraction of statistical dimensions, data analysis and output of statistical indexes, index analysis and issuing of business process diagnostic books and the like, generates the business process diagnostic books and then sends the business process diagnostic books to the client side. And issuing the service flow diagnostic book by the client.

The method and the system are based on a business architecture system, high requirements on business knowledge and data analysis capability of analysts are eliminated, tool use difficulty is reduced, indexes of all parts in the business architecture reflect business process resistance points, architecture resistance points and business promotion suggestions are mined, virtuous circle of iterative promotion of architecture assets is formed, business architecture assets are effectively promoted to land, business and technological innovation are enabled by optimizing the business architecture, and enterprise value growth is promoted.

It should be noted that the implementation, solved technical problems, implemented functions, and achieved technical effects of each module/unit and the like in the apparatus part embodiment are respectively the same as or similar to the implementation, solved technical problems, implemented functions, and achieved technical effects of each corresponding step in the method part embodiment, and are not described in detail herein.

Any of the modules, units, or at least part of the functionality of any of them according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules and units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, units according to the embodiments of the present disclosure may be implemented at least partially 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 may be implemented by any other reasonable means of hardware or firmware by integrating or packaging the circuits, or in any one of three implementations of software, hardware and firmware, or in any suitable combination of any of them. Alternatively, one or more of the modules, units according to embodiments of the present disclosure may be implemented at least partly as computer program modules, which, when executed, may perform the respective functions.

For example, any of the first response module 1110, the second response module 1120, and the analysis module 1130 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first response module 1110, the second response module 1120, and the analysis module 1130 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 may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware. Alternatively, at least one of the first response module 1110, the second response module 1120 and the analysis module 1130 may be at least partially implemented as a computer program module, which when executed, may perform a corresponding function.

FIG. 15 schematically shows a block diagram of an electronic device according to an embodiment of the disclosure. The electronic device shown in fig. 15 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 15, an electronic device 1500 according to an embodiment of the present disclosure includes a processor 1501 which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)1502 or a program loaded from a storage section 1508 into a Random Access Memory (RAM) 1503. Processor 1501 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset(s) and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and so forth. The processor 1501 may also include on-board memory for caching purposes. Processor 1501 may include a single processing unit or multiple processing units for performing different acts of a method flow in accordance with embodiments of the present disclosure.

In the RAM1503, various programs and data necessary for the operation of the electronic apparatus 1500 are stored. The processor 1501, the ROM 1502, and the RAM1503 are connected to each other by a bus 1504. The processor 1501 executes various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 1502 and/or RAM 1503. Note that the program may also be stored in one or more memories other than the ROM 1502 and the RAM 1503. The processor 1501 may also execute various operations of the method flows according to embodiments of the present disclosure by executing programs stored in one or more memories.

According to an embodiment of the present disclosure, electronic device 1500 may also include input/output (I/O) interface 1505, input/output (I/O) interface 1505 also being connected to bus 1504. The electronic device 1500 may also include one or more of the following components connected to the I/O interface 1505: an input portion 1506 including a keyboard, a mouse, and the like; an output portion 1507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 1508 including a hard disk and the like; and a communication section 1509 including a network interface card such as a LAN card, a modem, or the like. The communication section 1509 performs communication processing via a network such as the internet. A drive 1510 is also connected to the I/O interface 1505 as needed. A removable medium 1511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1510 as necessary, so that a computer program read out therefrom is mounted into the storage section 1508 as necessary.

According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 1509, and/or installed from the removable medium 1511. The computer program, when executed by the processor 1501, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the 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 present 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, a computer-readable storage medium may include the ROM 1502 and/or RAM1503 described above and/or one or more memories other than the ROM 1502 and RAM 1503.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. 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 separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. A business architecture detection method performed by a server, the business architecture including a process model, the process model including an activity model, the method comprising:

responding to an activity model identification from a client, determining business data and a common variable factor, and sending the common variable factor to the client, wherein a one-to-one or one-to-many mapping relation exists between an activity model corresponding to the activity model identification and a task model, and the common variable factor is a variable shared by a plurality of task models;

responding to a public variable factor identifier to be detected from the client, determining service data to be detected from the service data based on the public variable factor identifier to be detected, wherein the public variable factor to be detected corresponding to the public variable factor identifier to be detected is in the public variable factors; and

performing congestion point analysis on the service data to be detected to obtain a congestion point part of the service architecture, wherein the number of services processed by the congestion point part of the service architecture meets congestion point judgment conditions relative to the number of services processed by parts except the congestion point part of the service architecture,

wherein, the analyzing the blocking point of the service data to be detected to obtain the blocking point part of the service architecture comprises:

clustering the service data to be detected to obtain respective service quantity of a plurality of categories, wherein the categories respectively correspond to at least part of models in the process model; and

determining whether at least a portion of a model corresponding to each of the plurality of classes is a choke point portion based on a quantity of traffic for each of the plurality of classes,

wherein:

when the models corresponding to the plurality of categories are in a serial relationship in the active model, the blockage point determination condition includes: the traffic volume of the category is higher than a first reference threshold; and/or

When the models corresponding to the respective categories are in a parallel relationship in the active model, the congestion point determination condition includes: the amount of traffic of the class is below a second reference threshold.

2. The method according to claim 1, wherein the clustering the service data to be detected to obtain respective service quantities of a plurality of categories comprises:

and clustering the service data to be detected based on at least one appointed statistical dimension to obtain the service quantity of each appointed statistical dimension.

3. The method according to claim 2, wherein the at least one specified statistical dimension is determined based on common information of the traffic data to be detected.

4. The method of claim 1, wherein the business architecture further comprises a mockup, a first correspondence exists between the task model and the mockup;

the determining the service data comprises:

determining an associated task model included in the activity model corresponding to the activity model identification;

determining an associated transaction service corresponding to the associated task model from an internet technology architecture, wherein a second corresponding relation exists between the task model and the transaction service of the internet technology architecture;

determining an associated object service corresponding to the associated transaction service, wherein a third corresponding relation exists between the associated transaction service and the associated object service; and

and determining business data generated by the associated object service.

5. The method of claim 1, further comprising: and after the service data to be detected is subjected to blocking point analysis to obtain a blocking point part of the service architecture, removing an abnormal blocking point part from the blocking point part of the service architecture based on a service rule.

6. The method of claim 1, further comprising: and after the service data to be detected is subjected to congestion point analysis to obtain a congestion point part of the service architecture, generating a detection report, wherein the detection report comprises the congestion point part of the service architecture.

7. The method of claim 6, wherein the generating a detection report comprises:

replacing each parameter in a detection report template to generate the detection report packet, wherein the parameter of the detection report template includes at least one of: title, synopsis, content type, specific content template, and dynamic parameters.

8. A business architecture detection method performed by a client, the business architecture including a process model, the process model including an activity model, the method comprising:

sending an activity model identifier to a server so that the server determines service data and a common variable factor, and sending the common variable factor to the client, wherein a one-to-one or one-to-many mapping relationship exists between an activity model corresponding to the activity model identifier and a task model, and the common variable factor is a variable shared by a plurality of task models;

sending a public variable factor identifier to be detected to the server side so that the server side can determine service data to be detected from the service data based on the public variable factor identifier to be detected, wherein the public variable factor to be detected corresponding to the public variable factor identifier to be detected is in the public variable factors; and

receiving a detection report from the server, where the detection report includes partial information of a blocking point, and the partial information of the blocking point is determined by the server through performing blocking point analysis on the service data to be detected, where a service quantity processed by the blocking point part of the service architecture satisfies a blocking point judgment condition relative to a service quantity processed by a part of the service architecture other than the blocking point part, where the partial information of the blocking point is determined by the server through performing blocking point analysis on the service data to be detected and includes: clustering the service data to be detected to obtain respective service quantities of a plurality of categories, wherein the categories respectively correspond to at least part of the models in the process model; and determining whether at least a portion of the model corresponding to each of the plurality of classes is a choke point portion based on the amount of traffic in each of the plurality of classes, wherein: when the models corresponding to the respective categories are in a serial relationship in the active model, the congestion point determination condition includes: the traffic volume of the category is higher than a first reference threshold; and/or when the models corresponding to the respective classes are in a parallel relationship in the active model, the obstruction point determination condition includes: the amount of traffic of the class is below a second reference threshold.

9. A business architecture detection apparatus, the business architecture including a process model, the process model including an activity model, the apparatus comprising:

the first response module is used for responding to an activity model identifier from a client, determining business data and a common variable factor, and sending the common variable factor to the client, wherein one-to-one or one-to-many mapping relation exists between an activity model corresponding to the activity model identifier and a task model, and the common variable factor is a variable shared by a plurality of task models;

the second response module is used for responding to the public variable factor identifier to be detected from the client, determining the business data to be detected from the business data based on the public variable factor identifier to be detected, wherein the public variable factor to be detected corresponding to the public variable factor identifier to be detected is in the public variable factors; and

an analysis module, configured to perform a congestion point analysis on the service data to be detected to obtain a congestion point portion of the service framework, where a service quantity processed by the congestion point portion of the service framework satisfies a congestion point determination condition with respect to a service quantity processed by a portion other than the congestion point portion in the service framework, and the performing the congestion point analysis on the service data to be detected to obtain the congestion point portion of the service framework includes: clustering the service data to be detected to obtain respective service quantity of a plurality of categories, wherein the categories respectively correspond to at least part of models in the process model; and determining whether at least a portion of the model corresponding to each of the plurality of classes is a choke point portion based on the amount of traffic in each of the plurality of classes, wherein: when the models corresponding to the respective categories are in a serial relationship in the active model, the congestion point determination condition includes: the traffic volume of the category is higher than a first reference threshold; and/or when the models corresponding to the respective classes are in a parallel relationship in the active model, the obstruction point determination condition includes: the amount of traffic of the class is below a second reference threshold.

10. A business architecture detection apparatus, the business architecture including a process model, the process model including an activity model, the apparatus comprising:

the system comprises a first sending module, a second sending module and a third sending module, wherein the first sending module is used for sending an activity model identifier to a server so that the server determines service data and a common variable factor and sends the common variable factor to a client, a one-to-one or one-to-many mapping relation exists between an activity model corresponding to the activity model identifier and a task model, and the common variable factor is a variable shared by a plurality of task models;

the second sending module is used for sending the public variable factor identifier to be detected to the server side so that the server side can determine the service data to be detected from the service data based on the public variable factor identifier to be detected, wherein the public variable factor to be detected corresponding to the public variable factor identifier to be detected is in the public variable factors; and

a report receiving module, configured to receive a detection report from the server, where the detection report includes partial information of a blocking point, and the partial information of the blocking point is determined by the server performing blocking point analysis on the service data to be detected, where a number of services processed by the blocking point part of the service architecture satisfies a blocking point determination condition with respect to a number of services processed by a part of the service architecture other than the blocking point part,

wherein:

when the models corresponding to the respective categories are in a serial relationship in the active model, the congestion point determination condition includes: the traffic volume of the category is higher than a first reference threshold; and/or

11. A business architecture detection tool, the business architecture including a process model, the process model including an activity model, the tool comprising:

the architecture asset positioning module is used for searching the affiliated process model in the business architecture asset library according to the activity model identification and outputting the common variable factor related to all the task models included in the process model;

the data acquisition module is used for searching the service data generated by all the task models according to the entity models corresponding to all the task models and screening the service data according to the public variable factors selected by the user;

the dimension generation module is used for taking the extracted public information as a statistical dimension by searching a database table for recording the business data; and

a detection module for clustering the service data according to the statistical dimension to obtain a blocking point part of the service architecture,

wherein, the clustering the service data according to the statistical dimension to obtain the blocking point part of the service architecture comprises:

clustering the service data to obtain respective service quantities of a plurality of categories, wherein the categories correspond to at least part of the flow models respectively; and

wherein:

12. The business architecture detection tool of claim 11, further comprising:

a diagnostic book generation module to generate a diagnostic book that includes a choke point portion of the business architecture.

13. The business architecture detection tool of claim 12, further comprising:

the diagnostic book model setting module is used for defining the formatted content of the diagnostic book so as to generate the diagnostic book according to the formatted content, and the formatted content comprises at least one of the following: content type, dynamic parameters, and code snippet of presentation.

14. An electronic device, comprising:

one or more processors;

a storage device for storing executable instructions which, when executed by the processor, implement the method of any one of claims 1 to 8.

15. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, implement a method according to any one of claims 1 to 8.