CN109858050B

CN109858050B - Data model generation method and device

Info

Publication number: CN109858050B
Application number: CN201711241480.0A
Authority: CN
Inventors: 段伟希; 孔松; 葛澍; 孙金霞; 魏丽红; 梁双春; 陈雷雷; 黄蕊; 王灿如
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Suzhou Software Technology Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Suzhou Software Technology Co Ltd
Priority date: 2017-11-30
Filing date: 2017-11-30
Publication date: 2022-12-13
Anticipated expiration: 2037-11-30
Also published as: CN109858050A

Abstract

The application relates to the technical field of databases, in particular to a method and a device for generating a data model, which are used for solving the problems that in the prior art, the modeling efficiency is low and the error probability in the modeling process is high. The generation method of the data model provided by the application comprises the following steps: constructing a data definition structure body of each performance data based on the definition information of the performance data contained in the pre-stored performance data specification document; merging the data definition structures with the granularity information meeting the preset conditions to obtain a data entity definition structure; the method comprises the steps of constructing a topology association structure body used for describing the topology relation among network elements, establishing a first mapping relation between the topology association structure body and data entity definition structure bodies respectively corresponding to the network elements with the topology relation, combining the data entity definition structure bodies with the topology association structure body and having the first mapping relation with the topology association structure body, and generating a basic data model.

Description

Data model generation method and device

Technical Field

The present application relates to the field of database technologies, and in particular, to a method and an apparatus for generating a data model.

Background

Mobile communication network performance management is a core function of a mobile communication network management system. The mobile communication network management system collects network element performance data from each network element through a plurality of network interfaces, stores the network element performance data collected from each network element in a basic data model corresponding to each network element, and further calculates and assembles the basic data model corresponding to each network element to finally generate a performance analysis report for the network management system.

At present, manual modeling is mostly adopted when a basic data model is constructed. Firstly, according to the performance data specification format recorded in the performance data specification document, a model field is manually established, and a basic data model is constructed. However, performance data of one network element in a mobile communication network often reaches a scale of tens to hundreds, a model field needs to be manually established for the performance data of each network element, the modeling process is very complex and tedious, a large amount of workload needs to be consumed, and a situation that the model field is established incorrectly easily occurs. Moreover, the artificially constructed basic data model is often the data model with the finest granularity level, the data model with the upper report level can be generated only after a plurality of times of calculation and convergence processing, and each time the basic data model is calculated or converged, the association relation of different basic data models needs to be confirmed manually according to the report requirements of the upper application, and a corresponding model calculation algorithm or a corresponding convergence algorithm is selected, so that a large amount of workload is consumed in the calculation and convergence processes of each basic data model.

Therefore, in the prior art, the data model of the network management system is constructed by adopting a manual modeling mode, so that a large amount of manpower is consumed, the modeling efficiency is low, and the error probability in the modeling process is high.

Disclosure of Invention

The application provides a method and a device for generating a data model, which are used for solving the problems that a large amount of manpower is required to be consumed, the modeling efficiency is low and the error probability in the modeling process is high when the data model of a network management system is constructed in a manual modeling mode in the prior art.

The technical scheme provided by the application is as follows:

in a first aspect, a method for generating a data model includes:

constructing a data definition structure body of each performance data based on the definition information of the performance data contained in a pre-stored performance data specification document, wherein the data definition structure body of each performance data describes the granularity information attributed to the performance data, and the granularity information comprises the granularity of a network element;

merging the data definition structural bodies of which the granularity information meets the preset conditions to obtain a data entity definition structural body;

based on the topological relation among the network elements contained in the performance data specification document, constructing a topological correlation structural body used for describing the topological relation among the network elements, and establishing a first mapping relation between the topological correlation structural body and a data entity definition structural body respectively corresponding to the network elements with the topological relation;

and combining the data entity definition structural bodies with the first mapping relation with the topology association structural body to generate a basic data model based on the topology relation among the network elements described by the topology association structural body.

Further, the merging the data definition structures whose granularity information meets the preset condition to obtain the data entity definition structure specifically includes:

determining minimum granularity information adopted when the data definition structure bodies are combined;

selecting a data definition structure body corresponding to the performance data with the minimum granularity information from the data definition structure bodies of the performance data according to the granularity information which is described by the data definition structure body of the performance data and belongs to the performance data;

and merging the data definition structural bodies belonging to the same minimum granularity information into the selected data definition structural body corresponding to the performance data with the minimum granularity information to obtain a data entity definition structural body.

Further, the merging, based on the topological relation among the network elements described by the topological correlation structural body, the data entity definition structural body having the first mapping relation with the topological correlation structural body to generate a basic data model specifically includes:

and combining the data entity definition structure bodies with the first mapping relation with the topology association structure bodies in the data entity definition structure bodies with the first mapping relation with the topology association structure bodies according to the sequence from low to high of the topology relations among the network elements, wherein the corresponding network element definition structure bodies with the low levels are combined into the corresponding network element definition structure bodies with the high levels to obtain a basic data model.

Further, the method further comprises:

constructing a data association structure body used for describing association data based on the association information among the performance data contained in the performance data specification document; the association information comprises identification information of each performance data with association relation and a calculation formula for representing the association relation, wherein the association data is obtained by calculating each performance data with association relation through the calculation formula;

establishing a second mapping relation between the data association structure body and a first data definition structure body respectively corresponding to each performance data with the association relation;

after generating the base data model, the method further comprises:

determining a basic data model to which the first data definition structure belongs;

and merging the data association structural body which has the second mapping relation with the first data definition structural body into the determined basic data model to obtain an associated data model.

Further, the method further comprises:

constructing a data aggregation structure according to preset data aggregation information, wherein the data aggregation structure describes aggregation dimensions, aggregation modes and an aggregation formula adopted during aggregation of a data entity definition structure;

after the data definition structure bodies with the granularity information meeting the preset conditions are combined to obtain a data entity definition structure body, the method further comprises the following steps:

determining a first data entity definition structure body matched with the aggregation dimension described by the data aggregation structure body, and establishing a third mapping relation between the data aggregation structure body and the first data entity definition structure body;

and according to the convergence mode described by the data convergence structure body and the convergence formula described by the data convergence structure body, converging the first data definition structure body which has a third mapping relation with the data convergence structure body to generate a convergence data model.

Further, the aggregation mode described by the data aggregation structure body comprises:

converging the first data definition structure according to any combination of the convergence dimensions; or alternatively

Selecting a second data definition structure from the first data definition structures, wherein the second data definition structures are converged according to a fixed convergence dimension selected from the convergence dimensions, and third data definition structures except the second data definition structure in the first data definition structures are converged according to any combination of the convergence dimensions; or

And converging the first data definition structural body according to a combination mode of the convergence dimension set by a user.

Further, the converged data model is in a broad-table form.

Further, the data definition structure of each performance data also describes the identification information of the performance data;

after generating the base data model, the method further comprises:

collecting performance data files of each network element, and analyzing the attributive granularity information of the collected performance data files;

reading performance data from the collected performance data file, and converting the identification information of the read performance data into the identification information of standard performance data which can be identified by the generated basic data model;

matching the granularity information to which the acquired performance data file belongs and the identification information of the converted standard performance data with a field representing the granularity information and a field representing the identification information of the performance data in the generated basic data model respectively;

and storing the successfully matched performance data file in the generated basic data model.

Further, the method further comprises:

after the fact that the pre-stored performance data specification document or the preset data aggregation information is updated is determined, one or more of a data definition structural body, a topology association structural body and a data association structural body are changed according to the updated content in the pre-stored performance data specification document; or

And changing the data aggregation structure according to the updated content in the preset data aggregation information.

In a second aspect, an apparatus for generating a data model, the apparatus comprising:

the device comprises a construction module, a data definition structure body and a data processing module, wherein the construction module is used for constructing a data definition structure body of each performance data based on the definition information of the performance data contained in a pre-stored performance data specification document, the data definition structure body of each performance data describes the granularity information attributed to the performance data, and the granularity information comprises the granularity of a network element;

the first generation module is used for combining the data definition structural bodies with the granularity information meeting the preset conditions to obtain a data entity definition structural body;

the processing module is used for constructing a topological correlation structural body used for describing the topological relation among the network elements based on the topological relation among the network elements contained in the performance data specification document, and establishing a first mapping relation between the topological correlation structural body and a data entity definition structural body respectively corresponding to the network elements with the topological relation;

and the second generation module is used for combining the data entity definition structural bodies with the first mapping relation with the topological correlation structural body based on the topological relation among the network elements described by the topological correlation structural body to generate a basic data model.

Further, the first generating module is specifically configured to:

selecting a data definition structural body corresponding to the performance data with the minimum granularity information from the data definition structural bodies of the performance data according to the granularity information which the performance data described by the data definition structural body of the performance data belongs to;

Further, the second generation module is specifically configured to:

and according to the sequence of the topological relations among the network elements from low to high, merging the data entity definition structural bodies with the first mapping relation with the topological correlation structural body, wherein the corresponding network element definition structural bodies with the low levels are merged into the corresponding network element definition structural bodies with the high levels to obtain a basic data model.

Further, the building module is further configured to:

the processing module is further configured to:

establishing a second mapping relation between the data association structure body and first data definition structure bodies respectively corresponding to the performance data with the association relation; after the second generation module generates a basic data model, determining the basic data model to which the first data definition structure belongs;

the second generating module is further configured to:

Further, the building module is further configured to:

constructing a data aggregation structure according to preset data aggregation information, wherein the data aggregation structure describes aggregation dimensions, an aggregation mode and an aggregation formula adopted during aggregation of a data entity definition structure;

the processing module is further configured to:

after the first generation module merges the data definition structural bodies of which the granularity information meets the preset conditions to obtain a data entity definition structural body, determining a first data entity definition structural body matched with the convergence dimension described by the data convergence structural body, and establishing a third mapping relation between the data convergence structural body and the first data entity definition structural body;

the second generation module is further to:

converging the first data definition structure according to any combination of the convergence dimensions; or

And converging the first data definition structural body according to a combination mode of the convergence dimensionality set by a user.

Further, the converged data model is in a broad-table form.

the device further comprises:

the acquisition module is used for acquiring the performance data files of the network elements after the second generation module generates the basic data model, and analyzing the granularity information to which the acquired performance data files belong;

the conversion module is used for reading performance data from the collected performance data file and converting the identification information of the read performance data into the identification information of standard performance data which can be identified by the generated basic data model;

the matching module is used for respectively matching the granularity information to which the acquired performance data file belongs and the identification information of the converted standard performance data with a field representing the granularity information and a field representing the identification information of the performance data in the generated basic data model;

and the storage module is used for storing the successfully matched performance data file in the generated basic data model.

Further, the apparatus further comprises:

the updating module is used for changing one or more of the data definition structural body, the topology association structural body and the data association structural body according to the updated content in the pre-stored performance data specification document after the pre-stored performance data specification document or the preset data convergence information is determined to be updated; or changing the data aggregation structural body according to the updated content in the preset data aggregation information.

In a third aspect, an electronic device comprises: one or more processors; and one or more computer-readable media having stored thereon a program for executing the method of generating a data model, wherein the program, when executed by the one or more processors, performs the steps of the method according to any one of the first aspect.

In a fourth aspect, one or more computer readable media having stored thereon a program for performing a method of generating a data model, wherein the program, when executed by one or more processors, causes the processors to perform the method of any one of the first aspect.

The beneficial effects of the embodiment of the application are as follows: the data definition structure body of each performance data can be automatically constructed by analyzing the definition information of the performance data contained in the pre-stored performance data specification document, and then the data definition structure bodies with the granularity information meeting the preset conditions can be automatically combined according to the granularity information of each performance data described in each data definition structure body to obtain the data entity definition structure body. Therefore, the method and the device can automatically extract the definition information of the performance data contained in the performance data specification document and the topological relation among the network elements, construct the corresponding structural bodies, automatically combine the structural bodies meeting certain conditions, and finally obtain the basic data model, and the modeling process does not need the participation of users, thereby greatly reducing the workload of manually constructing model fields, improving the modeling efficiency, and avoiding errors possibly occurring when the model is manually constructed.

Drawings

FIG. 1 is a general architecture diagram of a system for generating a data model according to an embodiment of the present application;

FIG. 2 is a schematic flow chart illustrating a method for generating a base data model according to an embodiment of the present application;

FIG. 3 is a schematic flow chart diagram illustrating a method for generating a relational data model according to an embodiment of the present disclosure;

FIG. 4 is a schematic flowchart of a method for generating a converged data model according to an embodiment of the present application;

FIG. 5 is a flowchart illustrating a method for storing performance data files according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a data model generation apparatus according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without any creative effort belong to the protection scope of the present application.

Aiming at the problems existing in the manual modeling mode in the existing scheme, the method provides a scheme for automatically generating the data model, and only needs a user to configure performance data specification documents corresponding to various performance data to be stored in the data model respectively, and specify design method documents of the data model, and then, the content defined in the performance data specification documents can be automatically analyzed to generate corresponding data definition structural bodies, topology association structural bodies and other structural bodies related to the performance data, and meanwhile, the design methods contained in the design method documents of the data model can be analyzed to generate model design structural bodies, the model design structural bodies guide the integration and convergence between the data definition structural bodies and the other structural bodies related to the performance data, and finally, the required data model is automatically output.

First, referring to fig. 1, an overall architecture diagram of a system for generating a data model is provided for the embodiment of the present application, which specifically includes: the device comprises a model analysis module, a model construction module, an adaptive verification module and an acquisition and analysis module.

The model analysis module can read and analyze the content described in the pre-stored performance data specification document and the design method described in the design method document of the data model, and generates a corresponding model structural body. Specifically, the model parsing module may be divided into three sub-modules: the system comprises a data definition analysis module, a data association analysis module and a design mode analysis module.

The data definition analysis module is mainly used for analyzing definition information of each performance data contained in each performance data document, constructing a data definition structure body of each performance data, and further generating a data entity definition structure body.

And the data association analysis module is mainly used for analyzing the topological relation among the network elements and the association relation among the performance data contained in the performance data documents, constructing a topological association structure body, a data association structure body and the like, and establishing a mapping relation between the generated topological association structure body, the generated data association structure body and the like and the data definition structure body.

The design pattern analysis module is mainly used for analyzing the design methods contained in the design method documents of the data models, and further can be used for constructing a first model design structural body used for describing the construction strategy of the data entity definition structural body, a second model design structural body used for describing the aggregation strategy of each data entity definition structural body, a third model design structural body used for describing the generation strategy of the data models and the like.

The model construction module can combine or assemble various structural bodies constructed in the data definition analysis module and the data association analysis module in the model analysis module according to various methods described in various model design structural bodies constructed by the design mode analysis module in the model analysis module to generate various data models which can be used by an upper system, such as a basic data model, an associated data model, an assembled data model and the like.

The collecting and analyzing module is mainly used for collecting the performance data files of all network elements in the network and analyzing the data recorded in the performance data files.

The adaptive verification module is mainly used for converting the original performance data file collected in the collection and analysis module into a standard data file, and then carrying out adaptive processing on the standard data file and the data model generated in the model construction module.

In addition, the system may further include a Structured Query Language (SQL) adaptation module, and the SQL adaptation module may generate an SQL statement recognizable by the data model, and is used to access data, query, and update related content of the data model.

In the following, a detailed description is given to the method for generating a data model according to an embodiment of the present application in conjunction with a description of the overall architecture of the system for generating a data model.

Referring to fig. 2, a schematic flow chart of a method for generating a basic data model provided in the embodiment of the present application specifically includes:

step 201: and constructing a data definition structure body of each performance data based on the definition information of the performance data contained in the pre-stored performance data specification document, wherein the data definition structure body of each performance data describes the granularity information attributed to the performance data, and the granularity information comprises the granularity of the network element.

The number of the performance data specification documents may be at least one, and each performance data specification document may include definition information of one or more performance data, where the definition information may specifically include identification information of the performance data, network element information, time information, and region information described by the performance data.

The data definition structure of the performance data may be constructed according to definition information of the performance data analyzed from the performance data specification. The data definition structure body can describe identification information of the performance data and the like besides the granularity information of the performance data. The granularity information may further include spatial granularity and the like, and the spatial granularity may be determined based on the region information included in the definition information. Certainly, in actual application, other types of information may also be configured according to service requirements, which is not limited in the present application.

Step 202: and merging the data definition structural bodies of which the granularity information meets the preset conditions to obtain a data entity definition structural body.

In this embodiment, the data definition structures corresponding to the performance data may be combined according to a construction policy of the data entity definition structure described in the first model design structure generated in the design pattern analysis module, so as to obtain the data entity definition structure. The data entity definition structure obtained after merging may include information of each data definition structure before merging, such as identification information of performance data described in each data definition structure, network element granularity, time granularity, and the like, and the granularity information of the coarsest granularity in the granularity information corresponding to each data definition structure before merging may be used as the granularity information corresponding to the data entity definition structure obtained after merging.

Specifically, the constructing the policy may include merging with the network element and merging based on the minimum granularity information.

Merging with the network element may be to merge together data definition structures having the same network element granularity information described in the data definition structure corresponding to each performance data. For example, if the data definition structure a corresponding to the performance data 1 describes information of a network element of a Mobility Management Entity (MME), that is, a granularity of the network element to which the data definition structure a belongs is MME, and similarly, the data definition structure B corresponding to the performance data 2 also describes information of the network element MME, that is, a granularity of the network element to which the data definition structure B belongs is MME, the data definition structure a and the data definition structure B may be merged to obtain the data Entity definition structure by merging with the network element, and at the same time, the data definition structures a and B may not be retained before merging.

Merging based on the minimum granularity information can be understood as determining the minimum granularity information adopted when merging the data definition structural bodies, and merging the data definition structural bodies belonging to the same minimum granularity information in the data definition structural bodies corresponding to the performance data into the data definition structural bodies corresponding to the performance data with the minimum granularity information. The minimum granularity information may be a minimum network element granularity or a minimum time granularity, or may be a combination of the minimum network element granularity and the minimum time granularity. Of course, in actual application, the granularity information may also include spatial granularity and the like, and accordingly, any one or a combination of multiple granularities may also be selected to determine the corresponding minimum granularity information.

For example, taking the minimum granularity information as the minimum network element granularity as an example, assuming that the network element granularity to which the data definition structures C to E corresponding to the performance data 3 to 5 respectively belong is MmeFunction, epRpDynS6aMme, and EpRpDynS11Mme, respectively, where the eppdyns 6aMme and the EpRpDynS11Mme are lower-level granularities belonging to the MmeFunction with respect to the MmeFunction, if the MmeFunction is defined as the minimum granularity information, the data definition structures D and E corresponding to the eppdyns 6aMme and the EpRpDynS11Mme may be respectively merged into the data definition structure C corresponding to the MmeFunction, so as to obtain the data entity definition structure with the network element granularity of the MmeFunction. Meanwhile, the data definition structures D and E before merging may not be retained.

In addition, the two manners provided above may be combined in this embodiment, and specifically, the data definition structures corresponding to each performance data may be merged by using a merging manner of the same network element, and then merged with a coarser granularity by using a merging manner based on the minimum granularity information.

Step 203: and constructing a topology association structure body used for describing the topology relationship among the network elements based on the topology relationship among the network elements contained in the performance data specification document, and establishing a first mapping relationship among the topology association structure body and data entity definition structure bodies respectively corresponding to the network elements with the topology relationship.

Step 204: and combining the data entity definition structural bodies with the first mapping relation with the topology association structural body to generate a basic data model based on the topology relation among the network elements described by the topology association structural body.

Since the data entity definition structure specified in step 202 may correspond to the granularity of the network elements, and there are topological relationships such as inheritance, inclusion, and the like between the network elements, the data entity definition structures respectively corresponding to the network elements having a topological relationship may be merged according to the topological relationship between the network elements described in the topological correlation structure.

However, only the topological relation between the network elements can be known when the topological correlation structural body is generated, and there may be a plurality of generated topological correlation structural bodies, in order to determine which data entity definition structural bodies can be used to guide merging of each topological correlation structural body, in the present application, first a first mapping relation is established between each topological correlation structural body and the data entity definition structural bodies respectively corresponding to the network elements having the topological relation described by the topological correlation structural body, and further, when the basic data model is generated, the data entity definition structural bodies having the first mapping relation with the topological correlation structural body may be merged for any topological correlation structural body to obtain the basic data model.

Specifically, in the data entity definition structural body having the first mapping relationship with the topology association structural body, the data entity definition structural body of the corresponding network element at the low level may be merged into the data entity definition structural body of the corresponding network element at the high level according to the sequence from the low to the high of the topology relationship between the network elements, so as to obtain the basic data model.

For example, for an MME network Element, there is a inclusion relationship from an ME (Managed Element) to an MmeFunction to two interfaces of EpRpDynS6aMme and EpRpDynS11mm, so a topological association structure of ME- > MmeFunction- > EpRpDynS6aMme and a topological association structure of ME- > MmeFunction- > EpRpDynS11mm can be established, since it is known in the above example that the data definition structures corresponding to the EpRpDynS6aMme and EpRpDynS11mm can be merged into the data definition structure corresponding to the MmeFunction, further, the data definition structure corresponding to the ME and the data definition structure corresponding to the MmeFunction can be established with the topological association structure according to the topological relationship indicated in the topological association structure to establish a first mapping relationship, and then the data definition structures corresponding to the MmeFunction can be merged into the data definition structure corresponding to the ME to obtain a basic data model.

In a specific implementation, for a case that the topological relation between the network elements described in the topological correlation structure is relatively complex and has a plurality of levels, when the corresponding network element definition structure for the low-level data entity is merged into the corresponding network element definition structure for the high-level data entity, the corresponding network element definition structure for the low-level data entity may not be uniformly aggregated into the corresponding network element definition structure for the highest-level data entity, the topological relation may be divided into a plurality of levels (for example, for a case that the topological relation between the network elements is a- > B- > C- > D- > E- > F, the two levels of a- > B- > C and D- > E- > F may be divided), and then the merging of the data definition structures is performed according to the topological relation described in each level, where a specific merging process may refer to the contents set forth in the foregoing text, and will not be described in detail herein.

Therefore, the method and the device can automatically extract the definition information of the performance data contained in the performance data specification document and the topological relation among the network elements, construct the corresponding structural bodies, automatically combine the structural bodies meeting certain conditions, and finally obtain the basic data model, and the modeling process does not need user participation, thereby greatly reducing the workload of manually constructing the model field, improving the modeling efficiency, and avoiding errors possibly occurring when the model is manually constructed.

In the embodiment of the application, considering that not only the association between the granularity information but also the association between the performance data itself exist between the data definition structural bodies corresponding to the performance data, the association data model can be further generated on the basis of building the basic model.

In the following, a method for generating a relevant data model provided in an embodiment of the present application is described, which may refer to a flow diagram shown in fig. 3, and specifically includes the following steps:

it should be noted that, in a specific implementation, step 301 and step 201 in this embodiment may not be sequentially executed, and step 302 may be executed after the execution of step 201 is completed. Further, the subsequent steps 303 to 304 may be executed after the execution of the above step 204 is completed, that is, after the generation of the basic data model, the steps 303 to 304 are executed.

Step 301: and constructing a data association structure body for describing the associated data based on the associated information among the performance data contained in the performance data specification document.

The association information includes identification information of each performance data having an association relationship, a calculation formula for representing the association relationship, and the like. And the associated data is obtained by calculating each performance data with the association relation through the calculation formula.

Specifically, the form of the association relationship between the performance data may include a single table association and a multi-table association. The single table association may refer to an association between performance data belonging to the same performance data specification document and having the same granularity information, and the multi-table association may refer to an association between performance data belonging to different performance data specification documents and having different granularity information. For the performance data with single-table association, after calculation processing is performed through a calculation formula for representing an association relation, the generated association data can be new performance data with the same granularity information as the attribution of the performance data with single-table association; for the performance data with multi-table association, after calculation processing is performed through a calculation formula for representing the association relation, the generated association data may be new performance data different from the granularity information attributed to the performance data with multi-table association.

Step 302: and establishing a second mapping relation between the data association structure body and the first data definition structure bodies respectively corresponding to the performance data with the association relation.

In order to determine which data definition structures can be used for guiding to merge each data association structure, in the present application, first, a second mapping relationship is established between each data association structure and a data definition structure corresponding to each performance data having an association relationship described by the data association structure.

Step 303: and determining a basic data model to which the first data definition structure belongs.

Step 304: and merging the data association structure body which has the second mapping relation with the first data definition structure body into the determined basic data model to obtain an associated data model.

In the embodiment of the present application, the generated basic data model is obtained by merging data entity definition structures, and the data entity definition structures are obtained by merging data definition structures, so that the basic data model may include information described in the data definition structures constituting the basic data model, and accordingly, after the first data definition structure is determined, the basic data model to which the first data definition structure belongs may also be determined.

Furthermore, the data association structure can be merged into the determined basic data model to obtain an associated data model, the associated data model can be used for storing basic performance data, and can also automatically generate and store associated data obtained after the performance data with the association relation is calculated and processed by a calculation formula, so that the process of manually combing the association relation of the basic performance data and calculating the association index is avoided, and the modeling efficiency is improved.

In the embodiment of the application, after the data definition structures whose granularity information meets the preset conditions are combined to obtain the data entity definition structure, the data entity definition structures can be aggregated according to the aggregation strategy of the data entity definition structures described in the second model design structure generated in the design pattern analysis module to obtain the aggregated data model.

In the following, a method for generating a converged data model provided by the embodiment of the present application is introduced, and reference may be made to the flowchart shown in fig. 4, which specifically includes the following steps:

it should be noted that, in a specific implementation, step 401 and step 201 in this embodiment may not be sequentially executed in the execution order, and the subsequent steps 402 to 403 may be executed after the execution of step 202 is completed, that is, after the data entity definition structure is obtained, step 402 to step 403 are executed.

Step 401: and constructing a data aggregation structure according to preset data aggregation information, wherein the data aggregation structure describes the aggregation dimension, the aggregation mode and an aggregation formula adopted during aggregation of the data entity definition structure.

The aggregation dimensions described by the data aggregation structure body include dimensions of network elements, time, space and the like, and the dimensions can be respectively mapped to corresponding network element fields, time fields, space fields and the like in the data entity definition structure body. For example, taking the aggregation dimension as time as an example, a day dimension and a month dimension in the time may both be mapped to a field of "start time" in the data entity definition structure.

The convergence mode described by the data convergence structure mainly comprises a full connection mode, a main connection mode and a user-defined connection mode. The aggregation manner of the data entity definition structure in each mode will be described in detail later.

The aggregation formula adopted during aggregation described by the data aggregation structure body can be a default aggregation formula or a special aggregation formula. If no special convergence formula is configured independently during convergence processing, a default convergence formula can be adopted for convergence. Specifically, the default aggregation formula includes summing, averaging, maximizing, minimizing, counting, and the like. The special convergence formula can self-define a formula meeting the SQL standard according to requirements.

Step 402: and determining a first data entity definition structure body matched with the aggregation dimension described by the data aggregation structure body, and establishing a third mapping relation between the data aggregation structure body and the first data entity definition structure body.

Specifically, the aggregation dimension described in the data aggregation structure may be matched with the granularity information described in each data entity definition structure, for example, the network element dimension in the aggregation dimension may be matched with the network element granularity to which each data entity definition structure belongs, a first data definition structure matched with the aggregation dimension described in the data aggregation structure may be determined, and a third mapping relationship between the data aggregation structure and the first data entity definition structure may be established.

Step 403: and according to the convergence mode described by the data convergence structure body and the convergence formula described by the data convergence structure body, converging the first data definition structure body which has a third mapping relation with the data convergence structure body to generate a convergence data model.

Specifically, if the aggregation mode described by the aggregation structure is a full connection mode, the first data definition structure may be aggregated according to any combination of the aggregation dimensions;

if the aggregation mode described by the aggregation structure is a main connection mode, selecting a second data definition structure from the first data definition structure, wherein the second data definition structure is aggregated according to a fixed aggregation dimension selected from the aggregation dimensions, and a third data definition structure except the second data definition structure in the first data definition structure is aggregated according to any combination of the aggregation dimensions;

if the aggregation mode described by the aggregation structure is a user-defined connection mode, the first data definition structure can be aggregated according to a combination mode of the aggregation dimensions set by a user.

Specifically, the convergence formula adopted by the first data definition structure body during convergence may be a default convergence formula, or may also be a special convergence formula, which is not limited in the present application.

In addition, if the aggregation strategy of each data entity definition structure described in the second model design structure generated in the design pattern analysis module indicates that the aggregated data model adopts a wide table form, a large and complete wide table can be aggregated and generated on the basis of the basic data entity definition structure, and then the upper aggregated data model is further generated on the basis of the wide table. If the form of the broad table is not adopted, the basic data entity definition structure body directly and independently generates the converged data model respectively.

In addition, in specific implementation, after the model construction module generates the basic data model, the associated data model or the aggregated data model, whether the generated data model conforms to the specified database mode can be further detected, and after the conformity is determined, the final data model can be output.

In this embodiment of the present application, after the basic data model is generated, the data recorded in the performance data file of each network element acquired by the acquisition and analysis module may be correspondingly stored in the basic data model, which may specifically refer to the flowchart of the method shown in fig. 5:

step 501: and collecting the performance data files of the network elements, and analyzing the granularity information to which the collected performance data files belong.

The granularity information may include network element granularity, time granularity, and the like.

Specifically, for each performance data file, the network element information field included in the performance data file may be analyzed, and based on the resource topology relationship between network elements, the network element granularity of the finest granularity to which the performance data file belongs may be determined. The time information field contained in the performance data file can also be analyzed to obtain the time granularity to which the performance data file belongs.

Step 502: and reading the performance data from the collected performance data file, and converting the identification information of the read performance data into the identification information of the standard performance data which can be identified by the generated basic data model.

The identification information may be a name, a number, and the like of the performance data. Because the identification information in the collected performance data file may not be defined in a uniform format, the identification information of the performance data may be converted into the identification information of the standard performance data predefined in the basic data model, so that the basic data model can identify the performance data.

Step 503: and matching the granularity information to which the acquired performance data file belongs and the identification information of the converted standard performance data with a field representing the granularity information and a field representing the identification information of the performance data in the generated basic data model respectively.

Step 504: and storing the successfully matched performance data file in the generated basic data model.

Specifically, the network element granularity, the time granularity to which the performance data file belongs, and the identification information of the converted standard performance data may be matched with the field representing the network element granularity, the field representing the time granularity, and the field representing the identification information of the performance data in the generated basic data model one by one, and whether the performance data file can be adapted to the generated basic data model is analyzed as a whole, and if the matching is successful, the performance data file may be stored in the generated basic data model.

In addition, in the embodiment of the present application, since the performance data specification document may have a version update, when the performance data specification document is subjected to version update, the updated content in the performance data specification document may be determined, and one or more of the data definition structure, the topology association structure, and the data association structure may be modified based on the updated content. Moreover, when the preset data aggregation information needs to be updated due to service requirements, the data aggregation structure body can be changed according to the updated content in the preset data aggregation information. After each structure is updated to the latest version, various types of data models can still be automatically generated by referring to the modeling flow given above.

It should be noted that the terms "first," "second," and the like in the description of the embodiments of the present application are used for distinguishing between the description and the claims, and are not intended to indicate or imply relative importance or order to the claims.

Based on the same application concept, the embodiment of the present application further provides a device for generating a data model corresponding to the method for generating a data model, and because the principle of solving the problem of the device is similar to that of the method for generating a data model in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and repeated details are not repeated.

Referring to fig. 6, a schematic structural diagram of a device for generating a data model provided in an embodiment of the present application is shown, where the device specifically includes:

a building module 61, configured to build a data definition structure of each performance data based on definition information of the performance data included in a pre-stored performance data specification document, where the data definition structure of each performance data describes granularity information to which the performance data belongs, and the granularity information includes a network element granularity;

the first generating module 62 is configured to combine the data definition structural bodies whose granularity information meets the preset condition to obtain a data entity definition structural body;

a processing module 63, configured to construct a topology association structure body used to describe the topology relationship between the network elements based on the topology relationship between the network elements included in the performance data specification document, and establish a first mapping relationship between the topology association structure body and data entity definition structure bodies respectively corresponding to the network elements having the topology relationship;

a second generating module 64, configured to combine the data entity definition structure bodies having the first mapping relationship with the topology association structure body based on the topology relationship between the network elements described by the topology association structure body, and then generate a basic data model.

Further, the first generating module 62 is specifically configured to:

Further, the second generating module 64 is specifically configured to:

Further, the building module 61 is further configured to:

constructing a data association structure body for describing association data based on the association information among the performance data contained in the performance data specification document; the association information comprises identification information of each performance data with association relation and a calculation formula for representing the association relation, wherein the association data is obtained by calculating each performance data with association relation through the calculation formula;

the processing module 63 is further configured to:

the second generating module 64 is further configured to:

and merging the data association structure body which has the second mapping relation with the first data definition structure body into the determined basic data model to obtain an associated data model.

Further, the building module 61 is further configured to:

the processing module 63 is further configured to:

after the data definition structural bodies with the granularity information meeting the preset conditions are merged by the first generation module to obtain a data entity definition structural body, determining a first data entity definition structural body matched with the convergence dimension described by the data convergence structural body, and establishing a third mapping relation between the data convergence structural body and the first data entity definition structural body;

the second generation module 64 is further configured to:

Further, the converged data model is in a wide-table form.

the device further comprises:

an acquiring module 65, configured to acquire the performance data files of each network element after the second generating module generates the basic data model, and analyze granularity information to which the acquired performance data files belong;

the conversion module 66 is used for reading performance data from the collected performance data file and converting the identification information of the read performance data into the identification information of standard performance data which can be identified by the generated basic data model;

the matching module 67 is configured to match the granularity information to which the acquired performance data file belongs and the identification information of the converted standard performance data with a field representing the granularity information and a field representing the identification information of the performance data in the generated basic data model, respectively;

and the storage module 68 is used for storing the performance data file successfully matched in the generated basic data model.

Further, the apparatus further comprises:

an updating module 69, configured to, after it is determined that the pre-stored performance data specification document or the preset data aggregation information is updated, change one or more of the data definition structure, the topology association structure, and the data association structure according to the updated content in the pre-stored performance data specification document; or the data aggregation structure body is changed according to the updated content in the preset data aggregation information.

An embodiment of the present application further provides an electronic device, including: one or more processors; and one or more computer-readable media having stored thereon a program for executing the method of generating a data model, wherein the program, when executed by the one or more processors, implements the steps of the method as in any one of the above methods of generating a data model.

Embodiments of the present application also provide one or more computer-readable media having stored thereon a program for executing the method for generating a data model, wherein the program, when executed by one or more processors, causes the processors to execute the method as described in any one of the above methods for generating a data model.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims

1. A method for generating a data model, the method comprising:

2. The method according to claim 1, wherein the merging the data definition structures whose granularity information meets the preset condition to obtain the data entity definition structure specifically includes:

3. The method according to claim 1, wherein the generating a basic data model after merging the data entity definition structure bodies having the first mapping relationship with the topology association structure body based on the topology relationship between the network elements described by the topology association structure body specifically includes:

4. The method of claim 1, wherein the method further comprises:

after generating the base data model, the method further comprises:

5. The method of claim 1, wherein the method further comprises:

6. The method of claim 5, wherein the aggregation pattern described by the data aggregation structure comprises:

converging the first data definition structure according to any combination of the convergent dimensions; or

Selecting a second data definition structure from the first data definition structures, wherein the second data definition structures are converged according to a fixed convergence dimension selected from the convergence dimensions, and third data definition structures except the second data definition structure in the first data definition structures are converged according to any combination of the convergence dimensions; or alternatively

7. The method of claim 5, wherein the aggregated data model is in the form of a wide table.

8. The method of claim 1, wherein the data definition structure of each performance data further describes identification information of the performance data;

after generating the base data model, the method further comprises:

reading performance data from the collected performance data file, and converting the identification information of the read performance data into identification information of standard performance data which can be identified by the generated basic data model;

9. The method of claim 1, 4 or 5, further comprising:

10. An apparatus for generating a data model, the apparatus comprising:

the system comprises a construction module, a data analysis module and a data analysis module, wherein the construction module is used for constructing a data definition structure body of each performance data based on the definition information of the performance data contained in a pre-stored performance data specification document, wherein the data definition structure body of each performance data describes the granularity information attributed to the performance data, and the granularity information comprises the granularity of a network element;

the first generation module is used for combining the data definition structural bodies of which the granularity information meets the preset conditions to obtain a data entity definition structural body;

11. The apparatus of claim 10, wherein the first generation module is specifically configured to:

12. The apparatus of claim 10, wherein the second generation module is specifically configured to:

13. The apparatus of claim 10, wherein the build module is further configured to:

the processing module is further configured to:

establishing a second mapping relation between the data association structure body and a first data definition structure body respectively corresponding to each performance data with the association relation; after the second generation module generates a basic data model, determining the basic data model to which the first data definition structure belongs;

the second generating module is further configured to:

14. The apparatus of claim 10, wherein the build module is further to:

the processing module is further configured to:

the second generation module is further to:

15. The apparatus of claim 14, wherein the aggregation pattern described by the data aggregation structure comprises:

converging the first data definition structure according to any combination of the convergent dimensions; or alternatively

16. The apparatus of claim 14, in which the aggregated data model is in the form of a wide table.

17. The apparatus of claim 10, wherein the data definition structure of each performance data further describes identification information of the performance data;

the device further comprises:

the acquisition module is used for acquiring the performance data files of the network elements after the second generation module generates the basic data model, and analyzing the attributive granularity information of the acquired performance data files;

18. The apparatus of claim 10, 13 or 14, further comprising:

the updating module is used for changing one or more of the data definition structural body, the topology association structural body and the data association structural body according to the updated content in the pre-stored performance data specification document after the pre-stored performance data specification document or the preset data convergence information is determined to be updated; or the data aggregation structure body is changed according to the updated content in the preset data aggregation information.